awesome-gee-community-datasets Documentation

Community Datasets added by users and made available for use at large

1,244 stars HTML HTML 101 files Β· ~126,920 tokens #awesome-list#catalog#community-catalog#community-datasets#earth-engine#geospatial#geospatial-data#gis
RAW Doc

Technical Documentation: samapriya/awesome-gee-community-datasets

ℹ️ Provenance: Multi-Tier Ingestion Β· CodeWiki Reference

Overview (README.md)

[awesome-gee-community-datasets](https://gee-community-catalog.org/)







The awesome-gee-community-datasets are community-sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets. The project was started with the idea that a lot of research datasets are often unavailable for direct use and require preprocessing before use. This catalog lives and serves alongside the Google Earth Engine data catalog and also houses datasets that are often requested by the community and under a variety of open license.

You can read about the history and more in the Medium Post article here

Find details on how to navigate the catalog in the Medium Post article here

[Browse the Catalog](https://gee-community-catalog.org/)

https://gee-community-catalog.org/

Like, share and support the Github project. And you can now cite it too

Citation

text
Roy, S., Majumdar, S., & Swetnam, T. (2026). A community data commons for equitable Earth Science. Under review in AGU Earth's Future.
EarthArXiv preprint: https://doi.org/10.31223/X5WN4J

Catalog Release Citation

text
Roy, S., Majumdar, S.& Swetnam, T. (2026). samapriya/awesome-gee-community-datasets: Community Catalog (Version 4.0.0) [Computer software].
Zenodo. https://doi.org/10.5281/zenodo.21876800

Browser Index (`docs/browser/index.md`)

GEE Community Catalog Browser

The Google Earth Engine (GEE) Community Catalog Browser is a revolutionary visual gateway to Earth observation data, designed to transform how researchers, developers, and analysts discover and interact with geospatial datasets. This powerful companion to the existing catalog system offers an intuitive visual experience without sacrificing depth or comprehensiveness. Read about all the features in our substack blog

Start Exploring Today

Visit: gee-community-catalog.org/browse

Streamline Your Data Discovery Journey

Finding the perfect dataset among thousands shouldn't feel like searching for a needle in a digital haystack. The GEE Community Catalog now hosts over 4,000 datasets encompassing over half a petabyte of information. Our visual browser addresses this data discovery dilemma by providing thumbnail previews that allow you to quickly scan and identify relevant datasets, drastically reducing search time and allowing you to focus on what matters most – your analysis and research

Unified Access to Earth Engine's Vast Resources

For the first time, access both the Community Catalog and the main Earth Engine catalog through a single, unified interface. We've normalized thematic categorization across both repositories, eliminating workflow barriers and creating a seamless discovery experience regardless of data origin. This integration represents a significant technical achievement that transforms complex geospatial data exploration into an intuitive process for users at every skill level.

Powerful Features for Every Earth Engine Explorer

Visual-First Experience : Browse datasets visually with thumbnail previews that provide immediate insight into data characteristics and potential applications.

Advanced Filtering & Sorting : Customize your search with thematic or alphabetical sorting and keyword-based filters to quickly narrow down options to precisely what you need

Comprehensive Documentation : Each dataset includes direct links to documentation and sample implementation code, supporting both experts seeking granular metadata and newcomers building research workflows.

User-Centric Design : Enjoy features like session-based search history (with privacy protection), smooth pagination for browsing large result sets, and a dark mode option for comfortable viewing.

Contributing Index (`docs/contributing/index.md`)

Community Actions (Building the Catalog)

The Awesome GEE Community Catalog is an actively maintained and evolving project that serves a diverse user base with versatile backgrounds and needs. To efficiently address the requirements of all our users, evaluate change requests, and fix bugs, update datasets, I put in a lot of work and your contributions are helpful. The catalog is a collaborative effort, and I welcome your contributions! This catalog aims to provide a comprehensive and up-to-date list of community-driven datasets readily accessible within Google Earth Engine (GEE).

The Awesome GEE Community Catalog thrives on community contributions! Whether you've found a valuable dataset, spotted an error, or have a helpful tip to share, there are many ways to get involved. By contributing, you're not only helping us build a valuable resource for the GEE community, but you're also making it easier for others to find and utilize valuable Earth observation data.

**How you can contribute**

I know your time is valuable. That's why I've streamlined contributing to the Awesome GEE Community Catalog!

  • Clear Guides and Templates: I offer clear instructions and templates for reporting bugs, requesting changes, and participating in discussions. This saves you time by making sure your contributions are well-organized and easy for me to understand.
  • Focus on Finding, Not Formatting: I've designed the issue tracker and discussion board for easy navigation and search. This means you can spend less time formatting your contributions and more time focusing on the valuable information you're sharing.
  • Faster Support: By following my guidelines, you help me process your contributions quickly and efficiently. This means you get the help or support you need faster.

Ready to get started? Let's dive into the specific ways you can contribute!

**Creating an issue**

**Contributing**

Insiders Index (`docs/insiders/index.md`)

Why donate to the Community Catalog

The GEE Community Catalog is an Open Source and unfunded project that is developed and maintained by a one person team. You can read me and the work further in the about me section. While I started this as a personal side project in 2020, the realization was always present that this project has far reaching implications and applications in the larger geospatial community. I realized that this project could benefit not just the research community who are often producing valuable research products from their research but users who are interested in a share collection of community sources data sources. Behind the scenes most community catalog requests for adding a dataset to the community catalog is triaged by me, evaluated based on multiple factors such as license, data size and preprocessing complexity before I start the work on getting it ready.

The Insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above ☝ or submit a new dataset or tutorial request. If you fit under any of those categories fill this form to get insiders access. As an Insider, you'll be added to the "catalog-contributors" Google Group, giving you early access to datasets as they are processed, reviewed, and even those not yet released. You'll also receive occasional changelog and update emails, and have the ability to post questions, concerns, and thoughts to the group. And if we meet ask me for stickers to help spread the word 😊.

Over the last 4 years the project backend now includes over 100,000+ lines of code to often preprocess the dataset or make it ingest ready for Google Earth Engine and making it available for the geospatial community of GEE users. Currently the site serves over 500,000 requests from over 160+ countries. This work is built around creating a Community Data Commons and if you can and wish to support and donate to the project which goes towards simple things like cost of hosting, preprocessing feel free to do so using Github Sponsorship Tier setup for this project.

  Choose a sponsoring tier { .md-button .md-button--primary .mdx-sponsorship-button }

Projects Index (`docs/projects/index.md`)

Data Themes

The Awesome GEE (Google Earth Engine) Community Catalog is a valuable resource for researchers, developers, and environmental scientists. It organizes a diverse range of geospatial datasets into thematic groups, making them more accessible and findable. This structured approach allows users to efficiently locate datasets pertinent to their specific fields of study or interest.

!!! tip "Insiders Program and Insiders only datasets"

text
**Some datasets are part of the Insiders only datasets and they [can be found here](https://gee-community-catalog.org/insiders). The insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. The Insiders Program grants access to a few special selection of datasets. You can be part of the program click on the link :point_up: to find out more.**

Thematic Groups

The datasets in the Awesome GEE Community Catalog are categorized into several thematic groups, for example:

  • Population and Socioeconomic Datasets: These datasets provide crucial information on demographics, economic activities, and social indicators, which are essential for urban planning, public health, and socio-economic research.

  • Hydrology Datasets: This category includes data on water bodies, hydrological cycles, and water quality, supporting research and decision-making in water resource management, flood risk assessment, and environmental conservation.

  • Global Land Use and Land Cover Datasets: These datasets offer insights into land use patterns and changes in land cover over time, aiding studies in agriculture, forestry, urbanization, and climate change.

  • Climate and Weather Datasets: Essential for climate science, these datasets include historical and real-time data on weather patterns, temperature, precipitation, and other climatic factors.

???+ note

text
**While every effort has been made to place datasets in the most suitable thematic groups, it is acknowledged that some datasets may rightfully belong to more than one category. Users are encouraged to explore multiple themes if their research spans across different areas.**

Accessibility and Findability

The thematic grouping of datasets in the Awesome GEE Community Catalog enhances their accessibility and findability. By organizing datasets into clearly defined categories, the catalog simplifies the process of searching and identifying relevant data. This organization not only saves time but also ensures that users can easily locate the most appropriate datasets for their specific needs with the changelog recording periodic updates.

Publications Index (`docs/publications/index.md`)

Publications Using the Awesome GEE Community Catalog

The Awesome GEE Community Catalog is proud to be a resource for the global research community. This section highlights the growing list of peer-reviewed publications, pre-prints, and reports that have utilized datasets from our catalog for their innovative research.

We are thrilled to see the diverse applications of the community-contributed data and the impact it has on various fields. If you have used the Awesome GEE Community Catalog in your research and would like to be featured here, please use our citation guide and let us know

Published Research

Title Authors Journal / Venue Year Access Link
A comparison shopper’s guide to forest datasets Lee, Lucy G.; Pasquarella, Valerie J.; Glass, Benjamin; Morreale, Luca L.; Chung, Nina; Gao, Xiaojie; Thompson, Jonathan bioRxiv 2025 πŸ”“ Preprint Link
Impacts of climate change on the distribution pattern of Himalayan Rhubarb (Rheum australe D. Don) in Nepal Himalaya Paudel, Babu Ram; Subedi, Chandra Kanta; Ghimire, Suresh Kumar; Pyakurel, Dipesh; Rajbhandari, Meena; Chaudhary, Ram Prasad PLoS ONE, 20(5):e0323755 2025 πŸ”“ Open Access Link
A question-answering framework for geospatial data retrieval enhanced by a knowledge graph and large language models Li, Hao; Yue, Peng; Wu, Haoru; Teng, Baoxin; Zhao, Yongkun; Liu, Changfeng International Journal of Digital Earth, 18(1):2510566 2025 πŸ”“ Open Access Link
WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Ying, Qing; Poulter, Benjamin; Watts, Jennifer D.; Arndt, Kyle A.; Virkkala, Anna-Maria; Bruhwiler, Lori; Oh, Youmi; et al. Earth System Science Data, 17(6):2507–2534 2025 πŸ”“ Open Access Link
Global patterns and driving factors of the carbon accumulation rate in coastal wetlands Yan, Shao-Jun; Hao, Qian; Yan, Zhifeng; Yang, Ren-Min Earth-Science Reviews, 105199 2025 πŸ”’ Subscription Required Link
Towards the exploitation of HydroGNSS coherent channel: High sampling rate products Peng, Jilun; Cardellach, Estel; Li, Weiqiang; RibΓ³, Serni; Rius, Antonio IEEE Transactions on Geoscience and Remote Sensing 2025 πŸ”“ Open Access Link
Pillars of Cloud-Based Earth Observation Science Education Crowley, Morgan A.; Stuhlmacher, Michelle; Trochim, Erin D.; et al. AGU Advances, 4:e2023AV000894 2023 πŸ”“ Open Access DOI
Explainable Artificial Intelligence (XAI) for Interpreting the Contributing Factors Feed into the Wildfire Susceptibility Prediction Model Abdollahi, Arnick; Pradhan, Biswajeet Science of The Total Environment, 879:163004 2023 πŸ”“ Open Access DOI
Flood Susceptibility Mapping in District Jhang Using Remote Sensing and Advanced Machine Learning Techniques Shah, Adnan Abbas; Iqbal, Muhammad Farooq Proceedings of the 20th International Conference on Frontiers of Information Technology (FIT 2024) 2024 πŸ”’ Subscription Required IEEE Xplore
Large Disagreements in Estimates of Urban Land Across Scales and Their Implications Chakraborty, T.C.; Venter, Z.S.; Demuzere, M.; Zhan, W.; Gao, J.; Zhao, L.; Qian, Y. Nature Communications, 15:9165 2024 πŸ”“ Open Access DOI
How Accurate Are Existing Land Cover Maps for Agriculture in Sub-Saharan Africa? Kerner, H.; Nakalembe, C.; Yang, A.; Zvonkov, I.; McWeeny, R.; Tseng, G.; Becker-Reshef, I. Scientific Data, 11:306 2024 πŸ”“ Open Access DOI
Toward Field-Scale Groundwater Pumping and Improved Groundwater Management Using Remote Sensing and Climate Data Ott, Thomas J.; Majumdar, Sayantan; Huntington, Justin L.; Pearson, Christopher; Bromley, Matt; Minor, Blake A.; ReVelle, Peter; Morton, Charles G.; Sueki, Sachiko; Beamer, Jordan P.; Jasoni, Richard L. Agricultural Water Management, 302:109000 2024 πŸ”’ Subscription Required DOI
Challenges to the Sustainability of Urban Cultural Heritage in the Anthropocene: The Case of Suzhou, Yangtze River Delta, China Huang, Yanjun; Meadows, Michael E. Land, 14(4):778 2025 πŸ”“ Open Access DOI
Improving Smallholder Representation in Crop Maps Walker, Kevin, et al. SSRN Electronic Journal 2025 πŸ”“ Preprint Link
Improved Streamflow Simulations in Hydrologically Diverse Basins Using Physically-Informed Deep Learning Models Magotra, Bhawna; Saharia, Mahesh; Dhanya, C. T. Hydrological Sciences Journal, 70(5):775–788 2025 πŸ”’ Subscription Required DOI
The Biophysical Effects of Phenological Shifts Impact Land Surface Temperature for Corn Expansion in Northeastern China Ma, Yao, et al. Agricultural and Forest Meteorology, 362:110373 2025 πŸ”’ Subscription Required DOI
Data-Driven Assessment of Climate Change and Vegetative Cover Dynamics in Traditional Oases Baioni, Emanuela; Fiantanese, Gianluca; Porta, Giovanni M. Journal of Hydrology: Regional Studies, 59:102266 2025 πŸ”“ Open Access DOI
Genomic, Phenotypic and Environmental Correlates of Speciation in the Midwife Toads (Alytes) Ambu, Jacopo, et al. Molecular Ecology, 34 2025 πŸ”“ Open Access DOI
A Phenology-Based Evaluation of the Optimal Proxy for Cropland Suitability Based on Crop Yield Correlations from Sentinel-2 Image Time-Series Radocaj, Danijel; JuriΕ‘iΔ‡, Marijan Agriculture, 15(8):859 2025 πŸ”“ Open Access DOI
Mapping the Global Distribution and Conservation Status of Oasesβ€”Ecosystems of Pivotal Biocultural Relevance HernΓ‘ndez-AgΓΌero, JesΓΊs A., et al. PeerJ, 13:e18884 2025 πŸ”“ Open Access DOI
Global Extent and Change in Human Modification of Terrestrial Ecosystems from 1990 to 2022 Theobald, David M., et al. Scientific Data, 12:606 2025 πŸ”“ Open Access DOI
Inequality in infrastructure access and its association with health disparities Tu, Yanfang, et al. Nature Human Behaviour 2025 πŸ”’ Subscription Required DOI
Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya Abebe, Asnake Kassahun, et al. Remote Sensing, 17(10):1763 2025 πŸ”“ Open Access DOI
Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data Yang, Liuming, et al. Applied Sciences, 15(3):1474 2025 πŸ”“ Open Access DOI
Direct and Indirect Impacts of Urbanization on Biodiversity Across the World’s Cities Liu, Naiyi; Liu, Zihan; Wu, Yunhe Remote Sensing, 17(6):956 2025 πŸ”“ Open Access DOI
Forest Fuel Structure and Loading Along a Gradient of Gray-Phase Mountain Pine Beetle Severity in Jasper National Park, Alberta, Canada Skretting, Tristan, et al. Canadian Journal of Forest Research, 55:1–14 2025 πŸ”“ Open Access DOI
Simulating SAR Constellations Systems for Rapid Damage Mapping in Urban Areas: Case Study of the 2023 Turkey–Syria Earthquake Mestroni, Ilaria; Gamba, Andrea; Chini, Marco; Gamba, Paolo; Bovenga, Francesca International Journal of Applied Earth Observation and Geoinformation, 131:103579 2024 πŸ”’ Subscription Required Link
Mapping Agricultural Tile Drainage in the US Midwest Using Explainable Random Forest Machine Learning and Satellite Imagery Wan, Luwen; Kendall, Anthony D.; Rapp, Jeremy; Hyndman, David W. Science of The Total Environment, 950:175283 2024 πŸ”“ Open Access DOI
Relationships among Soil Moisture at Various Depths under Diverse Climate, Land Cover and Soil Texture Li, Nan; Skaggs, Todd H.; Ellegaard, Peter; Bernal, Andres; Scudiero, Elia Science of The Total Environment, 947:174583 2024 πŸ”’ Subscription Required DOI
Geomorphometric Analysis of the Kshetrapal Landslide in Chamoli, Uttarakhand, India Using the White Box Tool (WBT) and QGIS by Comparing Various DEMs Obtained from UAV and TLS Anand, Ashok International Journal of Geosciences, 16(1):31–60 2025 πŸ”“ Open Access DOI
Remote Sensing Insights into Urban–Rural Imbalance and Sustainable Development: A Case Study in Guangdong, China Zhang, Fushan; Zhang, Qingling; Xu, Minduan Sustainability, 17(5):2247 2025 πŸ”“ Open Access DOI
Exacerbated Heat Stress Induced by Urban Browning in the Global South Du, Huilin; Zhan, Wenfeng; Zhou, Bingbing; Ju, Yang; Liu, Zihan; et al. Nature Cities 2025 πŸ”’ Subscription Required DOI
m3LEO: A Multi-Modal Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data Papadomanolaki, Marios, et al. Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track 2024 πŸ”“ Preprint Link
Mapping Agricultural Tile Drainage in the US Midwest Using Explainable Random Forest Machine Learning and Satellite Imagery Wan, Luwen; Kendall, Anthony D.; Rapp, Jeremy; Hyndman, David W. Science of The Total Environment, 950:175283 2024 πŸ”“ Open Access DOI
High-Resolution Land Use Land Cover Dataset for Meteorological Modellingβ€”Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset Bessardon, Geoffrey; Rieutord, Thomas; Gleeson, Emily; PΓ‘lmason, Bolli; Oswald, Sandro Land, 13(11):1811 2024 πŸ”“ Open Access DOI
Permafrost Extent Sets Drainage Density in the Arctic Del Vecchio, Joanmarie; Palucis, Marisa C.; Meyer, Colin R. PNAS, 121(6):e2307072120 2024 πŸ”“ Open Access DOI
Automating Global Landslide Detection with Heterogeneous Ensemble Deep-Learning Classification GanerΓΈd, Alexandra Jarna; Franch, Gabriele; Lindsay, Erin; Calovi, Martina Remote Sensing Applications: Society and Environment, 33:101384 2024 πŸ”“ Open Access DOI
Evaluation of Ten Satellite-Based and Reanalysis Precipitation Datasets on a Daily Basis for Czechia (2001–2021) Paluba, Daniel; BližňÑk, VojtΔ›ch; MΓΌller, Miloslav; Stych, Premysl Preprint 2025 πŸ”“ Preprint Link
Photogrammetry to Assess the Recovery of a Forest: Case Study of Guadalupe Island Vera-Ortega, Laura Abigail; Hinojosa-Corona, Alejandro; Luna, Luciana; GudiΓ±o-Elizondo, NapoleΓ³n Investigaciones GeogrΓ‘ficas, 116:e60914 2025 πŸ”“ Open Access DOI
The Carbon Security Index: A Novel Approach to Assessing How Secure Carbon Is in Sagebrush Ecosystems Within the Great Basin O’Connor, Rory C.; Boyd, Chad S.; Naugle, David E.; Smith, Joseph T. Rangeland Ecology & Management, 97:169–177 2024 πŸ”“ Open Access DOI
Monitoring the Temporal-Spatial Changes of Vegetation Cover and Drivers in Tian Shan Ecoregions, China Shobairi, Seyed Omid Reza; Lingxiao, Sun; Haiyan, Zhang; Chunlan, Li; Jing, He; Beirami, Behnam Asghari Folia Forestalia Polonica, Series A – Forestry, 66(3):155–182 2024 πŸ”“ Open Access DOI

Reference Index (`docs/reference/index.md`)

Reference & Citation

While most datasets in our community catalog are citable themselves, it's important to note that the catalog as a whole should also be cited by users. Our citation information and release details are sourced from Zenodo and you can always find the latest DOI & Citation here.

We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release.

Citation

text
Roy, S., Majumdar, S., & Swetnam, T. (2026). A community data commons for equitable Earth Science. Under review in AGU Earth's Future.
EarthArXiv preprint: https://doi.org/10.31223/X5WN4J

Catalog Release Citation

text
Roy, S., Majumdar, S.& Swetnam, T. (2026). samapriya/awesome-gee-community-datasets: Community Catalog (Version 4.0.0) [Computer software].
Zenodo. https://doi.org/10.5281/zenodo.21876800

Earn your place in the citation

Search Features Index (`docs/search_features/index.md`)

Catalog Assistant

The Catalog Assistant is a custom-designed assistant for the Community Catalog, available at search.gee-community-catalog.org. Ask it questions in plain language and it returns summaries grounded in the catalog's own pages and dataset metadata β€” with links back to the source material, so you can verify every answer. You can still use the embedded search within the Community Catalog for basic keyword or text-match lookups.

Open the Catalog Assistant here.

???+ note

text
**If the assistant doesn't work as expected, try disabling any widget blocker extensions or using an incognito window to troubleshoot.**

Salient Features

  • Answers are grounded in the catalog β€” every summary links back to the source pages and dataset metadata, boosting trust and minimizing inaccuracies.
  • Supports multiple languages β€” ask in your preferred language and the results are summarized in the same language.
  • As with all generative assistants, prompt formulation matters. Slight changes in phrasing can often yield better results.

Limitations

  • Summaries are generated from a limited set of top results, so not every matching dataset may appear in a given answer.
  • Summaries may evolve over time as the context window changes, so your answer might vary between searches.

Previous deployment

Earlier versions of the assistant ran as a Vertex AI search widget built on RAG (Retrieval Augmented Generation), drawing on two data stores β€” the Community Catalog pages and a JSON data store β€” to keep its summaries grounded in reliable sources. That deployment was offered in Beta while we tested and refined it, and its output was a server-side–controlled widget that generated summaries from up to ten search results. Learn more about RAG in this earlier blog post. Notable changes from that phase:

  • Added a secondary data store (JSON) to speed up parsing of details like licenses and links.
  • Introduced a "Back to the Community Catalog" button for easy navigation back to the main catalog.
  • Implemented custom system instructions to improve output quality and relevance.

Tutorials Index (`docs/tutorials/index.md`)

Community Catalog Tutorials

In this section of the community catalog, we'll explore user contributed Tutorials featuring catalog datasets or a mix of community catalog and the main catalog.

The Importance of Tutorials

Tutorials are an essential component of any learning platform, as they provide a clear and concise guide on how to achieve specific goals or solve particular problems. In the context of Earth Engine, tutorials are particularly valuable because they help users:

  • Understand the capabilities and limitations of EE's tools and datasets
  • Learn how to manipulate and analyze data using EE's API and visualizations
  • Apply EE's resources to real-world problems and scenarios

Community Catalog Tutorials: A Valuable Resource

The Community Catalog tutorials are designed to be a comprehensive resource for learning about Earth Engine. These tutorials cover a range of topics, including:

  • Data discovery and manipulation
  • Visualization and analysis techniques
  • Integration with other tools and platforms
  • Best practices for working with EE's datasets

These tutorials are not only helpful for beginners but also serve as a valuable reference for experienced users looking to expand their skills or explore new applications.

Encouraging Participation: Contributing Code Examples, Tutorials, Edits, and More!

We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release.

Benefits of Contributing

By participating in the development of the Community Catalog, you can:

  • Gain recognition for your contributions and expertise
  • Enhance your skills and knowledge by working with other experts in the field
  • Help shape the direction of EE's tutorials and resources to meet the needs of the community
  • Contribute to the growth and development of the Earth Engine ecosystem

Conclusion

The Community Catalog is a vital resource for learning about Earth Engine, and its tutorials are an essential component. By contributing code examples, tutorials, edits, or other forms of participation, you can help shape the catalog and benefit from the collective knowledge and expertise of the community.

About (`docs/about.md`)

awesome-gee-community-catalog





The awesome-gee-community-catalog is an unfunded, open-source, grassroots project with a simple mission: to collect community-sourced and community-generated geospatial datasets and make them accessible β€” tied directly to an analysis platform, so that finding good data is never the bottleneck and the digital divide keeps narrowing. It lives and serves alongside the Google Earth Engine data catalog.

text
<h4 style="margin-top: 0; color: var(--md-accent-fg-color);">
    πŸ‘‹ On a Personal Note
</h4>

    Hi there! I'm **Samapriya Roy**, and I created and maintain the GEE Community Catalog.
    This has grown from a personal project into something much bigger thanks to this amazing community.
    If you find value in this work, star the
    <a href="https://github.com/samapriya/awesome-gee-community-datasets"
       target="_blank"
       style="color: var(--md-accent-fg-color); text-decoration: underline;">
        GitHub repo
    </a>
    as it helps others discover this catalog & keeps the effort visible.
    <a href="https://www.linkedin.com/in/samapriya/"
       target="_blank"
       style="color: var(--md-accent-fg-color); text-decoration: underline;">
        Connect with me
    </a>
    and start a chat; I love hearing how people are using this catalog & help me showcase your work!

Stay in the loop β€” sign up for email updates to get the latest catalog news and more.

Curious how it all began? Read the story of how this project started in this Medium article.

Community datasets, added by users and made available for everyone to use.

Like, share, and support the GitHub project β€” and now you can cite it, too.

Citation

text
Roy, S., Majumdar, S., & Swetnam, T. (2026). A community data commons for equitable Earth Science. Under review in AGU Earth's Future.
EarthArXiv preprint: https://doi.org/10.31223/X5WN4J

Catalog Release Citation

text
Roy, S., Majumdar, S.& Swetnam, T. (2026). samapriya/awesome-gee-community-datasets: Community Catalog (Version 4.0.0) [Computer software].
Zenodo. https://doi.org/10.5281/zenodo.21876800

About Us (`docs/about_us.md`)

About Us



Welcome to the Awesome GEE Community Catalog β€” a home for discovering and contributing geospatial datasets built for use with Google Earth Engine. It's an unfunded, open-source, grassroots project with a single mission: to collect community-sourced and community-generated geospatial data and make it accessible and analysis-ready, fostering collaboration and reducing the digital divide.

The catalog was created and is maintained by Dr. Samapriya Roy β€” still very much a one-person team. He is a Google Developer Expert for Google Earth Engine and the founder and CEO of Spatial Bytes, a geospatial and Geo AI consultancy, as well as a Research Scientist and Senior Geospatial Engineer at the Desert Research Institute and an advisor to NSF's NEON. Earlier in his career he was a Senior Product Manager at MAXAR and worked at Planet Labs. Dr. Roy holds a Ph.D. in Geography from Indiana University Bloomington and brings geospatial expertise as affiliate faculty at the University of HawaiΚ»i at Mānoa and a Designated Campus Colleague at the University of Arizona. A prolific open-source developer, he builds tools that make Earth observation data easier to find, access, and use β€” and champions open data and the idea of a geospatial commons that this catalog embodies.

The catalog is shaped by the community itself β€” through data requests, tutorial contributions, and the input, advice, and feedback of the #awesome community that uses it every day. It thrives on community participation and open-source principles, giving researchers, developers, and enthusiasts high-quality, analysis-ready data to build on. This year, the National Science Foundation (NSF) ACCESS program granted us 1.5 million Service Units (CPU core-hours) to continue this work through Jetstream2, an NSF project, which lets us preprocess datasets as requests come in.

Community Contributions

Our catalog is powered by the contributions of the GEE user base. Community members submit datasets, which are then reviewed, usually downloaded and preprocessed into Earth Engine–ready form, and finally added to the catalog for everyone to use. This collaborative approach keeps the collection diverse and rich β€” spanning topics from waterbodies and population distribution to drought monitoring and beyond. Every contribution expands the repository and makes it a stronger go-to resource for geospatial data. πŸ”

Update Schedule

We know how much reliable, current data matters. Some datasets refresh on a fixed cadence; others follow a more ad hoc schedule, updated as requests come in or as new information about a dataset becomes available. This flexibility lets us respond to the community's needs while keeping the data relevant and accurate. πŸ—“οΈ

We rely on the community to spread the word and share the catalog with others. Please cite and attribute the catalog using our citation β€” it makes the project more visible and helps it grow.

Want to help? Check out our Get Involved section.

Changelog (`docs/changelog.md`)

Changelog

Updated 2026-08-10

Updated 2026-08-06

Updated 2026-07-30

Updated 2026-07-01

Updated 2026-06-09

Updated 2026-06-02

Updated 2026-05-08

Updated 2026-03-27

Updated 2026-03-19

Updated 2026-02-16

Updated 2026-02-03

Updated 2026-01-27

Updated 2025-01-13

updated 2025-12-14

Updated 2025-12-03

Updated 2025-11-11

Updated 2025-10-31

Updated 2025-10-27

Updated 2025-10-01

Updated 2025-09-23

Updated 2025-09-11

Updated 2025-09-03

Updated 2025-08-20

Updated 2025-08-06

Updated 2025-07-23

Updated 2025-07-08

Updated 2025-06-29

Updated 2025-06-26

Updated 2025-06-16

Updated 2025-06-04

Updated 2025-06-03

Updated 2025-05-16

Updated 2025-04-02

Updated 2025-03-25

Updated 2025-02-18

Updated 2025-02-11

Updated 2025-01-28

Updated 2024-10-27

Updated 2024-10-22

Updated 2024-10-16

Updated 2024-10-03

Updated 2024-09-08

Updated 2024-08-30

Updated 2024-08-01

Updated 2024-07-03

Updated 2024-06-21

Updated 2024-06-13

Updated 2024-06-07

Updated 2024-05-23

Updated 2024-04-30

Updated 2024-04-22

Updated 2024-04-18

Updated 2024-04-02

Updated 2024-03-07

Updated 2024-02-22

Updated 2024-02-09

Updated 2024-01-18

Updated 2024-01-04

Updated 2023-11-30

Updated 2023-11-09

Updated 2023-11-02

Updated 2023-10-26

Updated 2023-10-19

Updated 2023-10-10

Updated 2023-10-04

Updated 2023-09-26

Updated 2023-09-20

Updated 2023-09-14

Updated 2023-08-30

Updated 2023-08-24

Updated 2023-08-17

Updated 2023-08-08

Updated 2023-07-20

Updated 2023-07-04

Updated 2023-06-30

Updated 2023-06-19

Updated 2023-06-07

Updated 2023-05-31

Updated 2023-05-15

Updated 2023-04-11

Updated 2023-03-28

Updated 2023-03-21

Updated 2023-03-09

Updated 2023-03-01

Updated 2023-02-07

Updated 2023-01-17

Updated 2023-01-08

Updates 2022-12-20

Updates 2022-12-01

Updates 2022-11-22

Updates 2022-11-01

Updates 2022-10-25

Updates 2022-10-11

Updates 2022-10-03

Updates 2022-09-26

Updates 2022-09-19

Updates 2022-09-12

Updates 2022-09-06

Updates 2022-08-30

Updates 2022-08-22

Updates 2022-08-16

Updates 2022-08-05

Updates 2022-08-01

Updates 2022-07-19

Updates 2022-07-07

Updates 2022-06-28

Updates 2022-06-14

Updates 2022-06-01

Updates 2022-05-27

Updates 2022-05-16

Updates 2022-05-08

Updates 2022-05-02

Updates 2022-03-14

Updates 2022-03-07

Updates 2022-02-28

Updates 2022-02-14

Updates 2022-02-07

Updates 2022-01-31

Updates 2022-01-24

Updates 2022-01-20

Updates 2022-01-10

Updates 2022-01-02

Updates 2021-12-20

Updates 2021-12-12

Updates 2021-11-29

Updates 2021-11-22

Updates 2021-11-15

Updates 2021-11-09

Updates 2021-11-01

Updates 2021-10-25

Updates 2021-10-18

Updates 2021-10-10

Updates 2021-10-04

Updates 2021-09-19

Updates 2021-09-12

Updates 2021-09-05

Updates 2021-08-31

Updates 2021-08-16

Updates 2021-08-01

Updates 2021-07-26

Updates 2021-07-17

Updates 2021-07-11

Code Of Conduct (`docs/code_of_conduct.md`)

Code of Conduct

The awesome-gee-community-catalog consists of community-sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets. The project was started with the idea that many research datasets are often unavailable for direct use and require preprocessing beforehand. This catalog lives and serves alongside the Google Earth Engine data catalog and also houses datasets that are frequently requested by the community, under a variety of open licenses.

You can read about the history and how this project started in the Medium Post article here

Community Datasets added by users and made available for use at large.

Please Cite and Acknowledge Use

Users of the Awesome GEE Community catalog must cite the work which allows the community and the project to grow. You can always find the latest citation here

Be Respectful and Inclusive

Treat all community members, regardless of their background or experience level, with respect and empathy. Harassment, discrimination, or offensive behavior in any form will not be tolerated. Everyone deserves to feel welcome and valued in our community.

Foster a Positive and Constructive Environment

Engage in discussions and debates in a constructive and positive manner. Disagreements are normal, but we expect community members to address conflicts professionally and respectfully. Focus on the ideas, not on personal attacks.

Encourage Diversity

We welcome contributions and ideas from people of all backgrounds, experiences, and identities. Embrace diversity and encourage the participation of individuals with different perspectives. A diverse community enriches everyone's learning and understanding.

Provide Constructive Feedback

When providing feedback on projects or contributions, do so with the intention of helping others improve. Be constructive and offer suggestions for improvement, but avoid overly negative or unhelpful feedback.

Respect Others' Work

Give proper credit to original authors and contributors. If you use or modify someone else's work, make sure to attribute them appropriately. Respect the licenses and terms associated with any resources you use or contribute.

Use Welcoming Language

Use inclusive language in all interactions. Avoid using offensive or exclusionary language, jokes, or slurs. Be mindful of the impact your words may have on others.

Report Inappropriate Behavior

If you witness or experience any behavior that violates this code of conduct, please report it to the project maintainers by reaching out on LinkedIn. All reports will be treated confidentially, and appropriate action will be taken as necessary.

Compliance

Participants who do not follow this code of conduct may face consequences, including but not limited to warnings, temporary bans, or permanent bans from the community.

Be Responsible

Community members should be responsible for their actions and their impact on others. If you make a mistake or hurt someone, apologize and try to make amends.

Our Pledge

In the interest of fostering an open and welcoming environment, we, as contributors and maintainers, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.

Acknowledgment

This code of conduct is adapted from the Contributor Covenant, version 2.0.

History (`docs/history.md`)

Building Data Commons

Communities are what communities build together. Google Earth Engine (GEE) is powerful not only because of what it can compute, but because of who gathers around it β€” a community that iterates in the open, shares its code, and learns in public. The Awesome GEE Community Catalog began as an attempt to give that spirit a home for data: a place where anyone could contribute a dataset and everyone could find one.

How it started

The catalog started with a single question. A user asked where to find Facebook's high-resolution population density maps β€” a dataset the official GEE catalog didn't carry. Hosted by Columbia University and resolving population down to 30 meters, it was exactly the kind of valuable, hard-to-reach layer that fell through the cracks. Rather than treat that gap as a dead end, we treated it as an experiment: what if the community itself maintained the datasets it needed?

That Facebook dataset became the first entry in the catalog β€” and, fittingly, one of its most frequently updated. The full origin story is captured in the foundational blog post.

The guiding principle

The catalog is built on an old idea about shared resources. Elinor Ostrom's Nobel-winning work on the governance of commons showed that communities can steward shared resources sustainably when they agree on the norms for using them β€” the same principle that carried open-source projects like Linux and collaborative knowledge bases like Wikipedia. What works for a physical commons, we believed, could work for a digital one.

So we set out to build a Community Data Commons: the Awesome GEE Community Catalog.

Why it matters

Earth Engine democratized access to planetary-scale processing. The catalog aims to do the same for the data β€” lowering the barrier to a growing, curated collection of public datasets so that finding the right layer is no longer the hard part of a project.

The real challenge, and the reason a commons model fits, is scale in both directions: the catalog has to serve continental datasets and one-off community contributions with equal care. It works because the Earth Engine ecosystem already runs on learning, adaptation, and iteration. The catalog simply gives that culture a shared shelf β€” a bridge between the people who publish data and the people who need it.

It is, and will remain, a collaborative effort. Its continued growth depends on the community that builds it. Contribute a dataset and help the commons grow.

Involved (`docs/involved.md`)

Stay updated & contribute

The Awesome GEE Community Catalog is created and maintained by Samapriya Roy, with data, examples, and tutorials contributed by our community. It's a commons: as a grassroots, open-source project, it survives only through the involvement of people like you. Below are a few ways to pitch in β€” bring a dataset, share an example, file a bug report, or open a pull request when you spot something to fix. Want updates? Never miss a new addition or an in-depth exploration β€” subscribe through our datacommons blog.

Choose your adventure

Medium Blogs (`docs/medium_blogs.md`)

Medium Blog posts

Find a list of associated blog posts related to GEE and community catalog here.

Stats (`docs/stats.md`)

Catalog Stats

Daily Stats

  • Total Size of catalog: 586.67 TB
  • Total images in catalog: 1,886,726
  • Total image collections in catalog: 795
  • Total Feature collections in catalog: 3,139
  • Last Run Date: 2026-10-22

Substack Blogs (`docs/substack_blogs.md`)

Substack Blogs

Find a list of associated blog posts related to GEE and community catalog here. To get post subscribe to our

Contributing Bug (`docs/contributing/bug.md`)

Bug report for dataset in community catalog

Bug reports are useful information for the catalog. This can range for anything from a spelling mistake that breaks integration to change in asset path that may not have been updated in the documentation for example, incorrect doucentation or citation reference and many more. These are different from dataset updates as they do not pertain to availability of updated data or release information.

To submit a bug report for an existing awesome-gee-catalog dataset use this link

Contributing Example (`docs/contributing/example.md`)

Submit example for dataset in community catalog

Examples are helpful in understanding different use cases for datasets as well as enabling rich visualization of an existing dataset from domain experts. The template allows you to point to an existing dataset and submit an example code link via code editor/colab link or otherwise for others to use. All example/code contributors get attribution in the code apart from dataset attributions which are already included.

To submit an example for an existing awesome-gee-catalog dataset use this link

Contributing Submit (`docs/contributing/submit.md`)

Submit or bring your data request to community catalog

The submit data request templates are further subdivded into two templates one for datasets that you might have created vs any dataset that might be valuable to the community catalog and you would like to submit for consideration. For both templates modify the markdown text as needed and fill in the pieces of information that is available to you as in the example below.

Contributing Update (`docs/contributing/update.md`)

Submit update request for dataset in community catalog

The submit updated data request templates is designed for requesting update to an existing data in the community catalog. This can range from new releases to continuous updates. Modify the markdown text in the template as needed and fill in the pieces of information that is available to you as in the example below.

To submit a suggested update to an existing awesome-gee-catalog dataset use this link

Insiders Insiders Program (`docs/insiders/insiders_program.md`)

Insiders program

The awesome GEE community catalog insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above :point_up: or submit a new dataset request for example using this template. If you fit under any of those categories fill this form to get insiders access.

What do you get when you sign up for the Insiders program?

  • You are added to a Google Group called catalog-contributors
  • This will give you early access to certain datasets as they are being processed, reviewed and some unreleased yet.
  • You will also recieve changelog and updates emails once in a while and you can post to the google group with questions, concerns and thoughts

Any and all support is appreciated you can sponsor the project using the sponsorship links as well as contributing and helping data curation for the catalog.You can now find a list of insiders only datasets within the catalog for easily locating these.

Projects GPWv4 (`docs/projects/GPWv4.md`)

Gridded Population of the World Version 4 Administrative Unit Center Points with Population Estimates

The Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11 consists of UN WPP-adjusted population estimates and densities for the years 2000, 2005, 2010, 2015 and 2020, as well as the basic demographic characteristics (age and sex) for the year 2010. The data set also includes administrative name, land and water area, and data context by administrative unit center point (centroid) location. The center points are based on approximately 13.5 million input administrative units used in GPWv4, therefore, these files require hardware and software that can read large amounts of data into memory.

Purpose:
To provide a vector (point) version of the input administrative units used in GPWv4 with population estimates, densities, 2010 basic demographic characteristics, and administrative name, area, and data context for use in data integration.

The documentation for this data set is available here

Use the following citation

text
Doxsey-Whitfield, Erin, Kytt MacManus, Susana B. Adamo, Linda Pistolesi, John Squires, Olena Borkovska, and Sandra R. Baptista. "Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4." Papers in Applied Geography 1, no. 3 (2015): 226-234.

Earth Engine Snippet

js
var gpw = ee.FeatureCollection("projects/sat-io/open-datasets/sedac/gpw-v4-admin-unit-center-points-population-estimates-rev11");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GPW-v4

Shared License:
This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Curated by: Samapriya Roy

Keywords: census geography, GPWv4, gridded population, uniform distribution

Last updated: 2021-04-07

Projects S2TSLULC (`docs/projects/S2TSLULC.md`)

ESRI 10m Annual Land Cover (2017-2025)

Time series of annual global maps of land use and land cover (LULC) was updated to v3 with global 10m land cover from 2017-2024. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These datasets produced by Impact Observatory and licensed by Esri were fetched from Impact Observatory

This map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory and you can find it in GEE here. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.

You can find more information starting with the first release here
Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail

Citation

text
Karra, Kontgis, et al. β€œGlobal land use/land cover with Sentinel-2 and deep learning.”
IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.

Class definitions

  1. Water
    Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.

  2. Trees
    Any significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).

  3. Flooded vegetation
    Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.

  4. Crops
    Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.

  5. Built Area
    Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.

  6. Bare ground
    Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.

  7. Snow/Ice
    Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.

  8. Clouds
    No land cover information due to persistent cloud cover.

  9. Rangeland
    Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.

For Accuracy Assessment information visit the ESRI release page

Class Value Remapped Value Land Cover Class Hex Code
1 1 Water #1A5BAB
2 2 Trees #358221
4 3 Flooded Vegetation #87D19E
5 4 Crops #FFDB5C
7 5 Built Area #ED022A
8 6 Bare Ground #EDE9E4
9 7 Snow/Ice #F2FAFF
10 8 Clouds #C8C8C8
11 9 Rangeland #C6AD8D

Earth Engine Snippet

js
var esri_lulc_ts= ee.ImageCollection("projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/ESRI-10M-LANDCOVER

Credits, Attributions and License

This dataset was produced by Impact Observatory for Esri. Β© 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:

Changelog

  • 2026-05-25 Added LULC 2025 to collection

  • 2025-07-08 Added LULC 2024 to collection

  • 2024-06-07 Added LULC 2023 to collection

  • Example path was changed

  • 2023-04-10 Added LULC 2022 to collection

Curated in GEE by: Samapriya Roy

Keywords: : landcover, landuse, lulc, 10m, global, world, sentinel, sentinel 2, impact observatory

Last updated: 2026-05-25

Projects Aces Bhutan (`docs/projects/aces_bhutan.md`)

ACES-Enhanced Rice Crop Maps for Bhutan (2016-2022)

Annual crop type rice maps for 2016-2022 for enabling improved food security decision making has remained a challenge in Bhutan. These maps were developed in collaboration with the Bhutan Department of Agriculture and SERVIR. Through focusing on advancing Science, Technology, Engineering, and Mathematics (STEM) in Bhutan, an effort to co-develop a geospatial application known as the Agricultural Classification and Estimation Service (ACES) was created. This dataset and paper focuses on the co-development of an Earth observation informed climate smart crop type framework which incorporates both modeling and training sample collection. The ACES web application and subsequent ACES modeling software package enables stakeholders to more readily use Earth observation into their decision making process. Additionally, this data set and paper offers a transparent and replicable approach for addressing and combating remote sensing limitations due to topography and cloud cover, a common problem in Bhutan. Lastly, this approach resulted in a Random Forest "LTE 555" model, from a set of 3,600 possible models, with an overall test Accuracy of 85% and F-1 Score of .88 for 2020. The model was independently validated resulting in an independent accuracy of 83% and F-1 Score of .45 for 2020.

Citation

text
Mayer, Timothy, Biplov Bhandari, Filoteo GΓ³mez MartΓ­nez, Kaitlin Walker, Stephanie A. JimΓ©nez, Meryl Kruskopf, Micky Maganini et al. "Employing the
agricultural classification and estimation service (ACES) for mapping smallholder rice farms in Bhutan."
Frontiers in Environmental Science 11 (2023): 1137835.

Earth Engine Snippet

js
var Bhutan_ACES_Rice_Maps = ee.ImageCollection("projects/servir-sco-assets/assets/Bhutan/Rice_Extent_Mapper/Predicted_Rice_Post_Processed_IC");
Map.setCenter(90.37, 27.51,8)
var palettes = require('users/gena/packages:palettes');

var snazzy = require("users/aazuspan/snazzy:styles");
snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale");

Map.addLayer(Bhutan_ACES_Rice_Maps,{min: 0,max: 1, palette: ["fee6ce","fdae6b","e6550d"]},
"ACES Rice Maps 2016-2022 ")

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ACES-BHUTAN

License

This dataset is available under a Creative Commons BY-4.0 license

Keywords: agriculture, land use, land cover, Bhutan, rice

Created & provided by: Mayer et al 2023, NASA SERVIR

Curated by: Mayer et al 2023, NASA SERVIR

Projects Af Cmask (`docs/projects/af_cmask.md`)

Ensemble Source Africa Cropland Mask 2016

In order to produce the most accurate cropland layer at 30 m spatial resolution for Africa, the cropland layers extracted from four remote sensing land cover datasets were integrated. The four datasets covered the period 2015 to 2017. Hence, the constructed cropland layer was produced for the nominal year 2016. To build the final layer, the cropland mapping accuracies of the four cropland layers were firstly investigated at the units of Agro-ecological zones. Then, the best cropland layers for all zones were spatially joined. The resulted cropland layer is a binary mask with higher overall accuracy than individual layers and more consistent with FAO official statistics. You can download the datasets here. You can read additional details from the paper here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

text
Nabil, Mohsen, Miao Zhang, Bingfang Wu, Jose Bofana, and Abdelrazek Elnashar. "Constructing a 30m African Cropland Layer for 2016 by Integrating
Multiple Remote sensing, crowdsourced, and Auxiliary Datasets." Big Earth Data 6, no. 1 (2022): 54-76.

Dataset Citation

text
Nabil, Mohsen; Zhang, Miao; Wu, Bingfang; Bofana, Jose; Elnashar, Abdelrazek (2021): A 30m African Cropland Layer for 2016 by Integrating Multiple
Remote sensing, Crowdsource, and Auxiliary Datasets.. figshare. Dataset. https://doi.org/10.6084/m9.figshare.13520141.v1

Earth Engine Snippet

js
var af_cropmask_2016 = ee.Image("projects/sat-io/open-datasets/landcover/AF_Cropland_mask_30m_2016_v3");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/AF-CROPLAND-MASK-30M-2016

License

This dataset is made available under the CC BY Attribution 4.0 International License.

Created by: Nabil, Mohsen; Zhang, Miao; Wu, Bingfang; Bofana, Jose; Elnashar, Abdelrazek

Curated in GEE by: Samapriya Roy

Keywords: Agriculture, Africa, cropland, cropland maps, agriculture land use

Projects Af Trees (`docs/projects/af_trees.md`)

High resolution map of African tree cover

This dataset leverages high-resolution satellite imagery obtained from a nano-satellite constellation, accessible in the tropics through Norway's International Climate and Forest Initiative (NICFI) programme. The primary goal of this dataset is to comprehensively map both forest and non-forest trees on a continental scale across Africa, surpassing the precision of previous endeavors in mapping woody vegetation at large scales.

Utilizing a machine learning approach, we employ 3 m PlanetScope satellite imagery to segment tree canopy cover across Africa, reaching the level of individual scattered trees. The dataset provides a detailed quantification of the contribution of trees located outside traditional forested areas to the overall tree cover within each country. Notably, at the continental scale, our analysis reveals that 29% of the total tree cover exists outside regions classified as forests in a contemporary state-of-the-art map based on Sentinel-2 10 m imagery. You can read the paper here

Citation

text
Reiner, F., Brandt, M., Tong, X. et al. More than one quarter of Africa’s tree cover is found outside areas previously
classified as forest. Nat Commun 14, 2258 (2023). https://doi.org/10.1038/s41467-023-37880-4

Dataset citation

text
Reiner, F., Brandt, M., Tong, X., Skole, D., Kariryaa, A., Ciais, P., Davies, A., Hiernaux, P., Chave, J., Mugabowindekwe, M.,
Igel, C., Oehmcke, S., Gieseke, F., Li, S., Liu, S., Saatchi, S., Boucher, P., Singh, J., Taugourdeau, S., … Fensholt, R.
(2023). Africa tree cover map [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7764460

Earth Engine Snippet

js
var tree_cover = ee.Image("projects/sat-io/open-datasets/PS_AFRICA_TREECOVER_2019_100m_V10")

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/PS-AFRICA-TREECOVER

License

This tree cover map is made freely available for non-commercial purposes. All usage of the data must be attributed and should be cited with the paper citation. Please see the NICFI license for full terms of usage, available here

Provided by: Reiner et al

Curated in GEE by: Samapriya Roy

Keywords: Africa, NICFI, Planet, Tree cover, Land cover

Last updated in GEE: 2024-01-18

Projects Agera5 Datasets (`docs/projects/agera5_datasets.md`)

AgERA5 (ECMWF) dataset, version 2.0

Daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on
the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5, version 2.0. Acquisition and pre-processing of the original ERA5 data is a complex and
specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and
modeling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were
aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1Β° spatial resolution. The correction to the 0.
1Β° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1Β° grid. The equations were
trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1Β° resolution. This way the data are tuned to the finer topography,
finer land use pattern, and finer land-sea delineation of the ECMWF HRES model. You can find additional information here and in the climate engine org dataset page here

Dataset Description

Spatial Information

Parameter Value
Spatial extent Global
Spatial resolution 9600 m (1/10-deg)
Temporal resolution Daily
Time span 1979-01-01 to present
Update frequency Updated daily with 7-day lag time

Variables

Variable Details
Wind speed ('Wind_Speed_10m_Mean_24h') - Units: Meters/second
- Scale factor: 1.0
Minimum temperature, 2m ('Temperature_Air_2m_Min_24h') - Units: Degrees Kelvin
- Scale factor: 1.0
Maximum temperature, 2m ('Temperature_Air_2m_Max_24h') - Units: Degrees Kelvin
- Scale factor: 1.0
Mean temperature, 2m ('Temperature_Air_2m_Mean_24h') - Units: Degrees Kelvin
- Scale factor: 1.0
Max temperature, 2m, daytime ('Temperature_Air_2m_Max_Day_Time') - Units: Degrees Kelvin
- Scale factor: 1.0
Mean temperature, 2m, daytime ('Temperature_Air_2m_Mean_Day_Time') - Units: Degrees Kelvin
- Scale factor: 1.0
Min temperature, 2m, nighttime ('Temperature_Air_2m_Min_Night_Time') - Units: Degrees Kelvin
- Scale factor: 1.0
Mean temperature, 2m, nighttime ('Temperature_Air_2m_Mean_Night_Time') - Units: Degrees Kelvin
- Scale factor: 1.0
Dewpoint temperature, 2m ('Dew_Point_Temperature_2m_Mean_24h') - Units: Degrees Kelvin
- Scale factor: 1.0
Precipitation ('Precipitation_Flux') - Units: Millimeters
- Scale factor: 1.0
Precipitation rain duration fraction ('Precipitation_Rain_Duration_Fraction') - Units: Count
- Scale factor: 1.0
Precipitation solid duration fraction ('Precipitation_Solid_Duration_Fraction') - Units: Count
- Scale factor: 1.0
FAO56 Reference ET ('ReferenceET_PenmanMonteith_FAO56') - Units: Millimeters
- Scale factor: 1.0
Snow depth ('Snow_Thickness_Mean_24h') - Units: Centimeters
- Scale factor: 1.0
Snow water equivalent ('Snow_Thickness_LWE_Mean_24h') - Units: Centimeters
- Scale factor: 1.0
Vapour pressure ('Vapour_Pressure_Mean_24h') - Units: hPa
- Scale factor: 1.0
Vapour pressure deficit ('VVapour_Pressure_Deficit_at_Maximum_Temperature') - Units: hPa
- Scale factor: 1.0
Downward solar radiation ('Solar_Radiation_Flux') - Units: J m-2d-1
- Scale factor: 1.0
Cloud cover ('Cloud_Cover_Mean_24h') - Units: Fraction
- Scale factor: 1.0
Relative humidity, 2m 06h ('Relative_Humidity_2m_06h') - Units: Percent
- Scale factor: 1.0
Relative humidity, 2m 15h ('Relative_Humidity_2m_15h') - Units: Percent
- Scale factor: 1.0

Citation

text
Copernicus Climate Change Service (2020): Agrometeorological indicators from 1979 to present derived from reanalysis. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.6c68c9bb (Accessed on DD-MMM-YYYY)

Earth Engine Snippet

text
/* Detailed source-code truncated for AI context efficiency. */

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/AGERA5-DATASETS

License

Data are subject to the License to Use Copernicus Products: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Keywords: climate, reanalysis, near real-time, ECMWF, precipitation, temperature

Dataset provider: Copernicus

Dataset curated in GEE by: Climate Engine Org

Projects Ai0 (`docs/projects/ai0.md`)

Global Aridity Index

The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.1 at 30 arc seconds or ~ 1km at the equator. You can read the paper here

Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals.

Data citation

text
Zomer, Robert; Trabucco, Antonio (2019): Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.
figshare. Dataset. https://doi.org/10.6084/m9.figshare.7504448.v6

Paper citation

text
Zomer, R.J.; Xu, J.;  Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.
Scientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1

Global-AI grid layers are available as monthly averages (12 data layers, i.e. one layer for each month) or as an annual average (1 data layer) for the 1970-2000 period.

Earth Engine Snippet

js
var aridity_index_yearly = ee.Image("projects/sat-io/open-datasets/global_ai/global_ai_yearly");
var aridity_index_monthly = ee.Image("projects/sat-io/open-datasets/global_ai/global_ai_monthly")

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-ARIDITY-INDEX

Aridity Index Value Climate Class
<0.03 Hyper Arid
0.03-0.2 Arid
0.2-0.5 Semi-Arid
0.5-0.65 Dry sub-humid
>0.65 Humid

License

The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use under the CC BY 4.0 Attribution 4.0 International license.

Data Website: You can download the data and description here

Curated in GEE by: Samapriya Roy

Keywords: aridity index, evapotranspiration, geospatial modeling

Last updated: 2022-09-02

Projects Airtemp (`docs/projects/airtemp.md`)

Global Daily near-surface air temperature

Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities. Zhang et al. (2022) developed a global dataset of daily Tmax and Tmin at 1-km resolution over land (50Β° S–79Β° N) through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The published dataset covered 2003–2020 and has since been extended, so the collection now spans 2003 to 2024.

This gridded 1 km resolution global (50Β° S ~ 79Β° N) daily maximum and minimum near-surface air temperature dataset (2003–2024) was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology.

Reported accuracies from the 10-fold cross-validation in the source publication give root mean square errors ranging from 1.20 to 2.44 Β°C for Tmax and 1.69 to 2.39 Β°C for Tmin, with accuracy affected primarily by land cover type, elevation range, and climate background.

You can read the published paper here and download the datasets here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Data preprocessing

The datasets were generated regionally and for tmax and tmin. The tmax and tmin were combined into a single collection for the regions generated. Additional metadata called "prop_type" was added to allow for filtering along with other metadata like the day of year and the actual date information for date based filtering. The source data are distributed as GeoTIFFs in the MODIS ellipse sinusoidal projection at 1 km, with temperature stored in units of 0.1 Β°C; the datasets were projected to EPSG 4326 before being ingested to Google Earth Engine.

Citation

text
Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., and Wang, L.: A global dataset of daily maximum and minimum
near-surface air temperature at 1 km resolution over land (2003-2020), Earth Syst. Sci. Data, 14, 5637-5649,
https://doi.org/10.5194/essd-14-5637-2022, 2022.

Dataset Citation

text
Zhang, Tao; Zhou, Yuyu (2022): A global 1 km resolution daily near-surface air temperature dataset (2003-2020).
Iowa State University. Collection. https://doi.org/10.25380/iastate.c.6005185.v1

Earth Engine Snippet

js
var africa = ee.ImageCollection("projects/sat-io/open-datasets/global-daily-air-temp/africa");
var australia = ee.ImageCollection("projects/sat-io/open-datasets/global-daily-air-temp/australia");
var eurasia = ee.ImageCollection("projects/sat-io/open-datasets/global-daily-air-temp/europe_asia");
var latin_america = ee.ImageCollection("projects/sat-io/open-datasets/global-daily-air-temp/latin_america");
var north_america = ee.ImageCollection("projects/sat-io/open-datasets/global-daily-air-temp/north_america");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-DAILY-NEAR-SURFACE-AIR-TEMP

License

These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt,
and build upon the material in any medium or format, so long as attribution is given to the creator.

Created by: Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., and Wang, L.

Curated in GEE by : Samapriya Roy

keywords: Air Temperature, land surface temperature (LST), SVCM-SP, MODIS, Global

Last updated on GEE: 2026-08-02

Projects Amazon Peat (`docs/projects/amazon_peat.md`)

Amazonian Peatland Extent

Tropical peatlands represent some of the most carbon-dense terrestrial ecosystems on the planet, playing a significant role in the global carbon cycle. However, substantial uncertainty exists in estimating their global extent and carbon storage potential. This dataset provides the first field-data-driven model of peatland distribution across the Amazon basin, developed using 2,413 ground reference points and a random forest model applied to a combination of remote sensing products.

The model predicts an Amazonian peatland extent of approximately 251,015 kmΒ² (95th percentile confidence interval: 128,671–373,359 kmΒ²), which is larger than that of the Congo Basin but 30% smaller than other recent model-based estimates. The dataset addresses key spatial gaps in ground reference data, particularly in regions like Brazil and Bolivia, where uncertainty remains high. The model highlights peatland areas with varying degrees of confidence, such as northern Peru, the Rio Negro basin, and Bolivia, providing a critical resource for future research and field validation efforts. You can read the paper here and download the datasets here

Data Highlights

  • Model Type: Random forest-based prediction using field reference points and remote sensing data.
  • Spatial Extent: Amazon basin, including northern Peru, Rio Negro basin, southwestern Orinoco basin, and Bolivia.
  • Predicted Peatland Extent: 251,015 kmΒ² (95% CI: 128,671–373,359 kmΒ²).
  • Model Performance: High confidence in certain areas like northern Peru; higher uncertainty in regions like Brazil, Bolivia, and the Rio Negro basin.
  • Field Reference Points: 2,413 ground points in and around Amazonian peatlands.
  • Predicted extent of Peat (organic soil of β‰₯ 30 cm thickness) across the study area (Amazon basin below 500 m and mean annual precipitation >1,390 mm) at 90 m resolution.

Citation

text
Hastie, A., Householder, J. E., Coronado, E. N. H., Pizango, C. G. H., Herrera, R., LΓ€hteenoja, O., de Jong, J., Winton, R. S., Corredor, G. A. A., Reyna, J., Montoya,
E., Paukku, S., Mitchard, E. T. A., Γ…kesson, C. M., Baker, T. R., Cole, L. E. S., Oroche, C. J. C., DΓ‘vila, N., Águila, J. D., … Lawson, I. T. (2024). A new data-driven map
predicts substantial undocumented peatland areas in Amazonia. Environmental Research Letters, 19(9), 094019. https://doi.org/10.1088/1748-9326/ad677b

Earth Engine Snippet

js
var Simple_AOI = ee.FeatureCollection("users/adamhastie50/Study_area_simplify");
var Amazon_peat_map = ee.Image("projects/sat-io/open-datasets/INT_Amazon_peat_map");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/AMAZONIA-PEATMAP

License

The datasets are available under a Creative Commons Attribution 4.0 International license.

Created by: Hastie et al 2024

Curated in GEE by: Hastie et al 2024 and Samapriya Roy

Keywords: Peat, Tropical Peat, Amazon basin

Last updated in GEE: 2024-10-21

Projects Annual Nlcd (`docs/projects/annual_nlcd.md`)

Annual NLCD Land Cover Dataset

The USGS Land Cover program integrates methodologies from the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment, and Projection (LCMAP), along with advanced deep learning, to create Annual NLCD a dataset suite that includes six products, each representing various U.S. land cover and change characteristics. The U.S. Geological Survey’s (USGS) Annual NLCD Collection 1.0 leverages innovations from the National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP) projects, incorporating modern deep learning techniques to deliver accurate, annual land cover and surface change data across the U.S.

Since 1985, Annual NLCD provides six products covering land cover, change, confidence, impervious surfaces, and spectral changes based on Landsat data, facilitating resource management and decision-making.These products leverage Landsat satellite data and are intended for applications in science, resource management, and decision-making, spanning from 1985 to 2024. This dataset supports various environmental analyses, such as urban growth studies, wetland monitoring, agricultural management, and climate impact assessments. Its annual updates and classification confidence features provide essential insights for long-term land use planning and change detection. You can acces User Guide here

Dataset Products and Descriptions

  • Land Cover: A sixteen-class system based on the modified Anderson Level II classification, categorizing dominant surface types like water, forests, and urban areas per pixel. RGB values visually differentiate these categories, ensuring compatibility across federal systems.

  • Land Cover Change: Tracks annual land cover shifts by comparing consecutive years, using concatenated codes (e.g., 9590 for wetland transitions) to identify changes. Areas without change retain their classification.

  • Land Cover Confidence: Provides confidence scores based on deep learning probabilities, indicating the model’s certainty in class assignments. Scores are uncalibrated but gauge classification reliability.

  • Fractional Impervious Surface: Measures the percentage of impermeable surfaces (0-100%) within a 30-meter pixel, informing developed area classifications like urban or suburban based on defined thresholds.

  • Impervious Descriptor: Differentiates urban, non-urban, and road surfaces within developed areas, offering a clear map of roads distinct from other urban features for detailed analysis.

  • Spectral Change Day of Year: Identifies the day significant spectral changes occur (values 1-366), pinpointing disturbances (e.g., fires) beyond seasonal variations, enabling temporal change tracking.

??? example "Expand to show Land Cover Classes"

text
| Class Value | Class Name                   | RGB Color |
| ----------- | ---------------------------- | --------- |
| 11          | Open Water                   | `#466b9f` |
| 12          | Perennial Ice/Snow           | `#d1def8` |
| 21          | Developed, Open Space        | `#dec5c5` |
| 22          | Developed, Low Intensity     | `#d99282` |
| 23          | Developed, Medium Intensity  | `#eb0000` |
| 24          | Developed, High Intensity    | `#ab0000` |
| 31          | Barren Land                  | `#b3ac9f` |
| 41          | Deciduous Forest             | `#68ab5f` |
| 42          | Evergreen Forest             | `#1c5f2c` |
| 43          | Mixed Forest                 | `#b5c58f` |
| 52          | Shrub/Scrub                  | `#ccb879` |
| 71          | Grassland/Herbaceous         | `#dfdfc2` |
| 81          | Pasture/Hay                  | `#dcd939` |
| 82          | Cultivated Crops             | `#ab6c28` |
| 90          | Woody Wetlands               | `#b8d9eb` |
| 95          | Emergent Herbaceous Wetlands | `#6c9fb8` |

Key Information

  • Data Type: UINT8, UINT16 (product-dependent)
  • Spatial Resolution: Based on the Landsat 30m grid
  • Temporal Coverage: 1985–2024 (updated annually)
  • Access: Products are accessible via multiple platforms, including MRLC Viewer and AWS S3.
Layer Name Class Values (Range) Min Max NoData Value
Land Cover Various land cover types (11, 12, ..., 95) N/A N/A 250
Land Cover Change Change class categories AABB AABB 9999
Land Cover Confidence Confidence levels 1 100 250
Fractional Impervious Surface Imperviousness percentage 0 100 250
Impervious Descriptor Impervious surface types (0: Non-Urban, 1: Roads, 2: Urban) N/A N/A 250
Spectral Change Day of Year Julian days of change 1 366 9999

Citation

text
U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products: U.S. Geological Survey data release,
https://doi.org/10.5066/P94UXNTS.

Earth Engine Snippet

js
var nlcd_landcover = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER

js
var nlcd_landcover_confidence = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CONFIDENCE");
var nlcd_landcover_change = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CHANGE");
var nlcd_fractional_impervious_surface = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/FRACTIONAL_IMPERVIOUS_SURFACE");
var nlcd_impervious_descriptor = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/IMPERVIOUS_DESCRIPTOR");
var nlcd_spectral_change_doy = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/SPECTRAL_CHANGE_DOY");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER-LAYERS

License

NLCD datasets are provided under a Creative Commons Zero v1.0 Universal license.

Provided by: USGS

Curated in GEE by: Samapriya Roy

Keywords: Land Cover, Land Change, Landsat, Deep Learning, Annual NLCD, USGS, Environmental Monitoring

Changelog

  • Added 2025 Annual NLCD updated layers to Collection 1.2
  • Added 2024 Annual NLCD layers

Last updated in GEE: 2025-07-08

Projects Anusplin (`docs/projects/anusplin.md`)

ANUSPLIN Gridded Climate Dataset

The ANUSPLIN Gridded Climate Dataset for Canada is a station based interpolated dataset produced using the Australian National University Spline (ANUSPLIN) model. It is produced by Agriculture and Agri-Food Canada and covers all of Canada. The dataset is available from 1950-2015 at daily and monthly timesteps for maximum temperature, minimum temperature, and total precipitation at 10km (0.1 degree) resolution. The ANUSPLIN Gridded Climate Dataset for Canada is a comprehensive and station-based interpolated dataset that has been meticulously produced using the Australian National University Spline (ANUSPLIN) model. Created by Agriculture and Agri-Food Canada, this dataset encompasses the entire geographical expanse of Canada and offers a valuable resource for researchers and climate enthusiasts alike.

Researchers and users interested in accessing the dataset can find it through the following external links:

It provides a detailed view of climate conditions with data available from 1950 to 2015, offering insights into daily and monthly variations in maximum temperature, minimum temperature, and total precipitation. This dataset offers a valuable resource for climate research, environmental studies, and various applications that require historical climate data for Canada and parts of the United States. Researchers can explore climate trends, assess climate change impacts, and derive valuable insights into the region's climate patterns using this comprehensive dataset.

Dataset description

Spatial Information

Parameter Value
Spatial extent United States and Canada
Spatial resolution 10-km (~0.1-deg)
Temporal resolution Daily and monthly
Time span 1950-01-01 to 2015-12-31
Update frequency Static

Variables

Variable Details
Minimum temperature, 2m (β€˜maxt’) - Units: Degrees Celsius
- Scale factor: 1.0
Maximum temperature, 2m (β€˜mint’) - Units: Degrees Celsius
- Scale factor: 1.0
Precipitation ('pcp') - Units: Millimeters
- Scale factor: 1.0

Citation

text
- Hutchinson, M. F., McKenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewska, E., Papadopol, P. (2009).
"Development and testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003."
American Meteorological Society(April): 725-741.

- McKenney, D. W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R., Price, D., Owen, T. (2011).
"Customized spatial climate models for North America." Bulletin of American Meteorological Society-BAMS December: 1612-1622.

Earth Engine Snippet

js
// Read in daily and monthly Image Collections and get single image from each collection
var anuspline_m_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-anusplin-monthly')
var anuspline_m_i = anuspline_m_ic.first()
var anuspline_d_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-anusplin-daily')
var anuspline_d_i = anuspline_d_ic.first()

// Print each single image to see bands
print(anuspline_m_i)
print(anuspline_d_i)

// Visualize each band from each single image
var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"]
var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse()
Map.addLayer(anuspline_m_i.select('pcp'), {min: 0, max: 200, palette: prec_palette}, 'pcp, monthly')
Map.addLayer(anuspline_m_i.select('mint'), {min: -30, max: 30, palette: temp_palette}, 'mint, monthly')
Map.addLayer(anuspline_m_i.select('maxt'), {min: -30, max: 30, palette: temp_palette}, 'maxt, monthly')

Map.addLayer(anuspline_d_i.select('pcp'), {min: 0, max: 10, palette: prec_palette}, 'pcp, daily')
Map.addLayer(anuspline_d_i.select('mint'), {min: -30, max: 30, palette: temp_palette}, 'mint, daily')
Map.addLayer(anuspline_d_i.select('maxt'), {min: -30, max: 30, palette: temp_palette}, 'maxt, daily')

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/ANUSPLIN-GRID

License

Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.

Keywords : climate, precipitation, temperature, AAFC, daily, monthly, reanalysis

Provider: Agriculture and Agri-Food Canada

Curator: ClimateEngine.org

Projects Aogcm Cmip6 (`docs/projects/aogcm_cmip6.md`)

Current and projected climate data for North America (CMIP6 scenarios)

Atmosphere-Ocean General Circulation Model (AOGCM) were developed to simulate climate variability on a wide range of time scales and are often tested in coupled simulations and data assimilation mode. You can read more about AOGCMs and CMIP6 here. The datasets on this page have been developed by AdaptWest, a project funded by the Wilburforce Foundation to develop information resources for climate adaptation planning. The data were generated using the ClimateNA software. ClimateNA uses data from PRISM and WorldClim for current climate, and downscales data from the Coupled Model Intercomparison Project phase 6 (CMIP6) database corresponding to the 6th IPCC Assessment Report for future projections.

Ensemble projections are average projections from 8 CMIP5 models (table below) that were chosen to represent all major clusters of similar AOGCMs. In addition to the ensemble projections, data are also provided from 9 individual AOGCMs (table below) that are representative of the larger ensemble. Nine individual models were selected to represent all major clusters of similar AOGCMs. A broader set of 8 AOGCMs were used to create the ensemble data. Ensemble projections are also provided here for a greater range of time periods and scenarios than are the projections from individual AOGCMs.

AOGCM Ensemble Models AOGCM Individual Models
ACCESS-ESM1-5 ACCESS-ESM1-5
BCC-CSM2-MR
CNRM-ESM2-1 CNRM-ESM2-1
CanESM5
EC-Earth3 EC-Earth3
GFDL-ESM4 GFDL-ESM4
GISS-E2-1-G GISS-E2-1-G
INM-CM5-0
IPSL-CM6A-LR
MIROC6 MIROC6
MPI-ESM1-2-HR MPI-ESM1-2-HR
MRI-ESM2-0 MRI-ESM2-0
UKESM1-0-LL UKESM1-0-LL

Data citation

text
AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution,
generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.

Paper citation

You can read the paper here and cite as as below

text
AdaptWest Project. 2022. Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). Available at adaptwest.databasin.org.
For further information and citation refer to:

Wang, T., A. Hamann, D. Spittlehouse, C. Carroll. 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS One 11(6): e0156720 https://doi.org/10.1371/journal.pone.0156720

Mahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 1-21. https://doi.org/10.1002/joc.7566

The current climatic variables included in the datasets for climate normals, AOGCM models and AOGCM ensemble model are listed below

Monthly Variables Description
tmin minimum temperature for a given month (Β°C)
tmax maximum temperature for a given month (Β°C)
tave mean temperature for a given month (Β°C)
ppt total precipitation for a given month (mm)

Earth Engine Snippet Climate variables

js
var climate_models_ppt = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_ppt");
var climate_models_tave = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tave");
var climate_models_tmax = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmax");
var climate_models_tmin = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_tmin");
var climate_normals_ppt = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_ppt");
var climate_normals_tave = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tave");
var climate_normals_tmax = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmax");
var climate_normals_tmin = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_tmin");
var aogcm_ensemble_ppt = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_ppt");
var aogcm_ensemble_tave = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tave");
var aogcm_ensemble_tmax = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmax");
var aogcm_ensemble_tmin = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_tmin");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CMIP6-CURRENT-FUTURE-SCENARIOS

Post processing for Google Earth Engine v7.3

  • All of the 9 individual AOGCM models are added to the collection pertaining to each climate variable and named Climate-Models_(Variable Name). The ensemble models are ingested as along with the climate normals.
  • Both AOGCM ensemble and individual models have date range and emission scenario type emission_scenario and start and end dates are added as property.
  • Since the Climate-Models-(Variable Name) collection consists of 9 individual models another metadata field is added to those collections namely model to filter by model name.
  • Since all Climate variables are monthly , an extra metadata called month is added to the climate normals, ensemble and the individual model collections for further slicing the data as needed.
  • Version number for model and period for 20 year and 30 year monthly variables are now included in the metadata.

Earth Engine Snippet Bioclimatic variables

js
var climate_models_bioclim = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Models_bioclim");
var aogcm_ensemble_bioclim = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/AOGCM-ensemble_bioclim");
var climate_normals_bioclim = ee.ImageCollection("projects/sat-io/open-datasets/CMIP6-scenarios-NA/Climate-Normals_bioclim");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CMIP6-CURRENT-FUTURE-BIOCLIMATIC

There are a total of 33 bioclimatic variables included for the collections and models , the reference table is included below and you can filter using the metadata property bioclim_variable and the property names from the table.

Bioclimatic Variables Description
MAT mean annual temperature (Β°C)
MWMT mean temperature of the warmest month (Β°C)
MCMT mean temperature of the coldest month (Β°C)
TD difference between MCMT and MWMT, as a measure of continentality (Β°C)
MAP mean annual precipitation (mm)
MSP mean summer (May to Sep) precipitation (mm)
AHM annual heat moisture index, calculated as (MAT+10)/(MAP/1000)
SHM summer heat moisture index, calculated as MWMT/(MSP/1000)
DD_0 degree-days below 0Β°C (chilling degree days)
DD5 degree-days above 5Β°C (growing degree days)
DD_18 degree-days below 18Β°C
DD18 degree-days above 18Β°C
NFFD the number of frost-free days
FFP frost-free period
bFFP the julian date on which the frost-free period begins
eFFP the julian date on which the frost-free period ends
PAS precipitation as snow (mm)
EMT extreme minimum temperature over 30 years
EXT extreme maximum temperature over 30 years
Eref Hargreave's reference evaporation
CMD Hargreave's climatic moisture index
MAR mean annual solar radiation (MJ m-2 d-1) (excludes areas south of US and some high-latitude areas)
RH mean annual relative humidity (%)
CMI Hogg’s climate moisture index (mm)
DD1040 (10<DD<40) degree-days above 10Β°C and below 40Β°C
Tave_wt winter (December to February) mean temperature (Β°C)
Tave_sp spring (March to May) mean temperature (Β°C)
Tave_sm summer (June to August) mean temperature (Β°C)
Tave_at autumn (September to November) mean temperature (Β°C)
PPT_wt winter (December to February) precipitation (mm)
PPT_sp spring (March to May) precipitation (mm)
PPT_sm summer (June to August) precipitation (mm)
PPT_at autumn (September to November) precipitation (mm)
PPT_at autumn (September to November) precipitation (mm)

Known issues:

  1. Some discontinuity in precipitation values occurs along the US/Canada border due to edge-matching issues between the PRISM data for the two nations.

  2. Mean annual solar radiation (MAR) data are provisional and are slated to be revised in an upcoming release of the ClimateNA software.

License

These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.

Changelog

  • Updated to v7.3
  • Added 20 year periods apart from 30 year periods

Data Website: You can download the data and description here

Explore the data in R-Shiny apps here

Created by: AdaptWest Project, Wang, T., A. Hamann, D. Spittlehouse, C. Carroll

Curated in GEE by: Samapriya Roy

Keywords: climate change, global circulation models, gridded climate data, north america,emission scenarios,climate variables

Last updated: 2023-03-24

Aqualink is a philanthropically funded system to help people manage their local marine ecosystems in the face of increasing Ocean temperatures. The system consists of satellite-connected underwater temperature sensors and photographic surveys to allow for remote collaboration with scientists across the world. This export was created as a subset of datasets and sites available from aqualink.org as part of making ocean temperature readings in situ truly possible and globally accessible. The aqualink buoy is a collaboration of aqualink with sofarocean to deploy this buoy as sensors that capture ocean temperature both at surface and at varying depths. They are also capable of measuring things like wave height and wind conditions among other things. You can read about aqualink buoys here

The datasets were downloaded and processed using the pyaqua tool I wrote earlier and you can read about aqualink and the pyaqua tool here. These represent sea surface temperature as well as temperature at depth. These were generated only for deployed buoys and are exported CSVs are then imported into Google Earth Engine. The datasets have timestamp and value for said variable which can be used further to assess conditions over time.

This is a one year subset only for 56 deployed sites from 2020-01-04 to 2021-01-04 and is a subset for users to test and the format and duration of data might change in the future as this project evolves

Data Citation

Citation rules will vary by journal or need but a good example would be

text
aqualink.org (2021). Clerke Reef West side, Australia SST. Retrieved from https://aqualink.org/sites/1218

Earth Engine Snippet

js
var top_temp = ee.FeatureCollection("projects/sat-io/open-datasets/open-ocean/aqualink_top_temp_2020");
var bottom_temp = ee.FeatureCollection("projects/sat-io/open-datasets/open-ocean/aqualink_bottom_temp_2020");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/AQUALINK-SUBSET-2020

License

All work and data under the aqualink project are under an MIT license and free and open to the community.

Created by: aqualink org

Curated by: Samapriya Roy

Keywords: : aqualink, buoy, temperature, sea surface temperature, sst, wave, oceans, bleaching, coral reefs, extreme-sea-level

Last updated: 2022-01-05

Projects Argo (`docs/projects/argo.md`)

Argo Float Data(Subset)

Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. Each instrument (float) spends almost all its life below the surface. The name Argo was chosen because the array of floats works in partnership with the Jason earth observing satellites that measure the shape of the ocean surface. (In Greek mythology Jason sailed on his ship the Argo in search of the golden fleece). To learn more about Argo, how it works, its data and technology, and its scientific and environmental impact, click here.

Citation

These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System.

The general Argo DOI is below.

Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182

If you used data from a particular month, please add the month key to the end of the DOI url to make it reproducible. The key is comprised of the hashtag symbol (#) and then numbers. For example, the key for August 2020 is 76230. The citation would look like:

Argo (2020). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC) – Snapshot of Argo GDAC of August 2020. SEANOE. https://doi.org/10.17882/42182#76230

ArgoVis citation

Argovis API was used to parse through and get to the datasets, you can cite argovis using the following

text
Tucker, T., D. Giglio, M. Scanderbeg, and S.S. Shen, 2020: Argovis: A Web Application for Fast Delivery,
Visualization, and Analysis of Argo Data. J. Atmos. Oceanic Technol., 37 (3), 401-416
https://doi.org/10.1175/JTECH-D-19-0041.1

Argo Float data tables

Argo float dataset has been parsed into a small subset of about 20,000 feature collections flattened into a single collection with over 12.1 million features with total distinct argo float count at 866. The argo float property variables and GEE property names are listed below

Property GEE Property Property Type
Platform ID pid integer
Instrument Type inst_typ integer
date date integer
date added date_added integer
profile number profile_number string
maximum pressure max_pres float
pres_max_for_TEMP pmax_temp float
pres_min_for_TEMP pmin_temp float
pres_max_for_PSAL pmax_psal float
pres_min_for_PSAL pmin_psal float
Temperature temp float
Salinity psal float
Pressure pres float

Earth Engine Snippet

js
var argo = ee.FeatureCollection("projects/sat-io/open-datasets/argo-subset");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/ARGOFLOAT-SUBSET

License

Argo data are freely available without restriction and are released in a model similar to a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.

Created by : International Argo Program, Global Data Assembly Centre

Curated in GEE by: Samapriya Roy

Keywords: float, Argo, global ocean observing system, ocean circulation, in-situ, ocean pressure, sea water salinity, sea water temperature, multi-year, weather climate and seasonal observation, global-ocean

Last updated : 2021-07-30

Projects Aster (`docs/projects/aster.md`)

ASTER Global Digital Elevation Model (GDEM) v3

The first version of the ASTER GDEM, released in June 2009, was generated using stereo-pair images collected by the ASTER instrument onboard Terra. ASTER GDEM coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99 percent of Earth's landmass.

The improved GDEM V3 adds additional stereo-pairs, improving coverage and reducing the occurrence of artifacts. The refined production algorithm provides improved spatial resolution, increased horizontal and vertical accuracy. The ASTER GDEM V3 maintains the GeoTIFF format and the same gridding and tile structure as V1 and V2, with 30-meter postings and 1 x 1 degree tiles. You can read more about the product in the user guide here

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Source Data structure

The data are in Geotiff format, with each file divided into 1x1 degree tiles. To allow for adding a single image instead of a collection output, the zip files were unzipped and a single composite tif file was generated.

Citation

text
NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global
Digital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/ASTER/ASTGTM.003

Earth Engine Snippet

js
var gdem = ee.Image("projects/sat-io/open-datasets/ASTER/GDEM");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ASTER-GDEM

License

All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. This license can thus be treated similar to a public domain CC0 license. ASTER GDEM Version 3 (ASTGTM V003) was released on August, 5, 2019 and contains no redistribution requirements. The LP DAAC kindly requests that you properly cite the data in your research.

Created by: NASA, METI, AIST, Japan Spacesystems and U.S./Japan ASTER Science Team

Curated in GEE by: Samapriya Roy

Keywords: ASTER, DEM, elevation, remote sensing

Projects Astwbd (`docs/projects/astwbd.md`)

ASTER Global Water Bodies Database (ASTWBD) Version 1

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information.

The ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.

To generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake water bodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. You can find above mentioned detail along with description here

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Source Data structure

The data are in Geotiff format, with each file divided into 1x1 degree tiles. To allow for adding a single image instead of a collection output, the zip files were unzipped and a single composite tif file was generated.

Citation

text
NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global
Digital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/ASTER/ASTGTM.003

Earth Engine Snippet

js
var astwbd_att = ee.Image("projects/sat-io/open-datasets/ASTER/ASTWBD_ATT");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/ASTWBD

License

All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. This license can thus be treated similar to a public domain CC0 license. ASTER GDEM Version 3 (ASTGTM V003) was released on August, 5, 2019 and contains no redistribution requirements. The LP DAAC kindly requests that you properly cite the data in your research.

Created by: NASA, METI, AIST, Japan Spacesystems and U.S./Japan ASTER Science Team

Curated in GEE by: Samapriya Roy

Keywords: ASTER, DEM, elevation, remote sensing, Water Bodies Database

Projects Avhrr Ltdr (`docs/projects/avhrr-ltdr.md`)

ESA Fire Disturbance Climate Change Initiative (CCI)

The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.

The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide. You can download the datasets from this link

The Spatial resolution of this BA product is 0.05 degrees, which is the resolution of the AVHRR-LTDR input data.

The Coordinate Reference System (CRS) used is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid and using a Plate Carrée projection with geographical coordinates of equal pixel size.This product is distributed in global monthly files, grouped by year.

Details of the Pixel product

The pixel product is composed of 5 files:

  • JD.tif: Day of first detection (Julian Day) of the burned area
  • CL.tif: Confidence level of burned area detection
  • BA.tif: Burned area corresponding to the proporcion of the pixel that is calculated as burned.
  • OB.tif: Number of observations, i.e. times that the pixel has been observed in the month.
  • xml: Metadata of the product

Pixel Attribute Summary

Attribute Units Data Type Notes
Date of the first detection (JD) Day of the year (1-366) Float - 0: Not burned - 1-366: Day of first detection for burned pixel - -1: Not observed in month - -2: Not burnable (water, bare land, urban, snow/ice)
Confidence level (CL) 0-100 Float - 0: Low burn probability - 1-100: Increasing burn probability confidence - -1: Not observed in month - -2: Not burnable (water, bare land, urban, snow/ice)
Burned Area (BA) Square meters Float - 0-N: Burned area within pixel cell - -1: Not observed in month - -2: Not burnable (water, bare land, urban, snow/ice)
Number of observations (OB) 0-31 Int16 - 0-31: No-cloud observations in pixel - 0: Not observed - -2: Not burnable (water, bare land, urban, snow/ice)

Citation

text
Chuvieco, E.; Pettinari, M.L.; OtΓ³n, G. (2020): ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1.Centre for Environmental Data Analysis, 21 December 2020. doi:10.5285/b1bd715112ca43ab948226d11d72b85e.
https://dx.doi.org/10.5285/b1bd715112ca43ab948226d11d72b85e

Earth Engine Snippet

js
var BA = ee.ImageCollection("projects/sat-io/open-datasets/ESA/AVHRR-LTDR/BA");
var CL = ee.ImageCollection("projects/sat-io/open-datasets/ESA/AVHRR-LTDR/CL");
var JD = ee.ImageCollection("projects/sat-io/open-datasets/ESA/AVHRR-LTDR/JD");
var OB = ee.ImageCollection("projects/sat-io/open-datasets/ESA/AVHRR-LTDR/OB");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/ESA-FIRE-DISTURBANCE-CCI

License

You can find license information here

Created by: Chuvieco, E.; Pettinari, M.L.; OtΓ³n, G, ESA

Curated in GEE by : Samapriya Roy

keywords: ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS Essential Climate Variable

Last modified: 2020-12-21

Last updated on GEE: 2024-04-01

Projects Bii (`docs/projects/bii.md`)

Biodiversity Intactness Index (BII)

The Biodiversity Intactness Index (BII) measures biodiversity change using abundance data on plants, fungi and animals worldwide. The Index shows how local terrestrial biodiversity responds to human pressures such as land use change and intensification. Generated by Impact Observatory, in collaboration with Vizzuality, these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2025. Biodiversity Intactness data is based on the PREDICTS database of spatially referenced observations of biodiversity across 26,000 sites from over 500 studies.

Biodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods white paper entitled β€œGlobal 100m Projections of Biodiversity Intactness for the years 2017-2025 (v1.1)” authored by Francis Gassert, Joe Mazzarello, Sam Hyde.

The extension of the dataset through 2025 is announced here.

Data download: source.coop/vizzuality/biodiversity-intactness-100m-v1-1

Ancillary dataset Citation

text
Hudson, Lawrence N., Tim Newbold, Sara Contu, Samantha LL Hill, Igor Lysenko, Adriana De Palma, Helen RP Phillips et al. "The database of the
PREDICTS (projecting responses of ecological diversity in changing terrestrial systems) project." Ecology and evolution 7, no. 1 (2017): 145-188.

Dataset citation

text
Gassert, F., Mazzarello, J., & Hyde, S. (2026). Global 100m projections of biodiversity intactness for the years 2017–2025 (v1.1)
[Technical white paper]. Vizzuality and Impact Observatory.
https://data.source.coop/vizzuality/biodiversity-intactness-100m-v1-1/biodiversity-intactness-100m-white-paper-v1.1.pdf

Earth Engine Snippet

js
var bii_v1_1 = ee.ImageCollection("projects/ebx-data/assets/earthblox/IO/BII_V1_1");

// blue-green palette
var visParams = {
  min: 0,
  max: 1,
  palette: ['f7fcfd','e5f5f9','ccece6','99d8c9','66c2a4',
            '41ae76','238b45','006d2c','00441b']
};

// creating a mean composite
var bii_composite = bii_v1_1
  .mean();

// selecting the 2025 image
var bii_2025 = bii_v1_1
  .filterDate('2025-01-01', '2026-01-01');

Map.setCenter(0, 0, 2);

Map.addLayer(bii_composite, visParams, 'BII Composite');
Map.addLayer(bii_2025, visParams, 'BII 2025');

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/biodiversity-ecosystems-habitat/BIODIVERSITY-INTACTNESS-INDEX

License

The datasets are made available under the CC BY 4.0 license

Keywords: biodiversity, terrestrial, intactness, global, pressures

Dataset producer/licensor/processor: Impact Observatory and Vizzuality

Data hosted in Earth Engine by: Earth Blox

Contact: [email protected]

Changelog

  • v1.1 (2026): Temporal coverage extended from 2017-2020 to 2017-2025; PREDICTS training data updated to 26,000 sites from over 500 studies; new asset path projects/ebx-data/assets/earthblox/IO/BII_V1_1; updated white paper, citation and source.coop download.
  • v1.0: Initial release covering 2017-2020 (projects/ebx-data/assets/earthblox/IO/BIOINTACT).

Projects Bii Africa (`docs/projects/bii_africa.md`)

Biodiversity Intactness Index (BII) for sub-Saharan Africa

The Biodiversity Intactness Index (BII) is an indicator of ecosystem condition that assesses the impact of human land uses and activities on the abundance of a wide variety of species contributing diverse functions, capturing the multidimensional nature of biodiversity in a way that can be compared across multiple scales and time periods. It indicates the average remaining proportion of 'intact' populations of indigenous species in a particular area, relative to a pre-colonial/industrial reference state, on a scale from zero (nothing remains) to 1 (intact).

Previous assessments of ecosystem condition are criticised for being 'top-down': based on global, decontextualised pressure-impact relationships that extrapolate across data-poor taxa and regions, such as sub-Saharan Africa. This novel bottom-up approach overcomes critical data gaps and limitations of top-down biodiversity models by quantifying biodiversity intactness using the 'bii4africa' expert-elicited dataset that was previously published based on the knowledge of 200 experts in African fauna and flora. The bii4africa dataset contains experts' standardised estimates of the impact of sub-Saharan Africa's predominant land uses on diverse functional groupings of species that represent ~50,000 terrestrial vertebrates and vascular plants. Ten spatial datasets were integrated to map these land uses, which were combined with bioregional lists of indigenous taxa and the associated bii4africa data to map the BII across sub-Saharan Africa.

The data can be interactively explored and downloaded for individual countries using this Earth Engine app and the fighshare repo.

Citation

text
Clements, H.S., Biggs, R., De Vos, A. et al. A place-based assessment of biodiversity intactness in sub-Saharan Africa.
Nature (2025). https://doi.org/10.1038/s41586-025-09781-7

Dataset citation

text
Clements, Hayley; Biggs, Reinette; De Vos, Alta; San, Emmanuel Do Linh; Hempson, Gareth; Linden, Birthe; et al. (2025). Data and R code for:
A place-based assessment of Sub-Saharan Africa's biodiversity intactness. figshare. Dataset. https://doi.org/10.6084/m9.figshare.29773169.v1

Earth Engine Snippet

text
/* Detailed source-code truncated for AI context efficiency. */

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/biodiversity-ecosystems-habitat/BII-SUBSAHARAN-AFRICA

License

The datasets are made available under the CC BY 4.0 license

Created by: Clements et al 2025

Curated in GEE by: Geethen Singh, Hayley Clements and Samapriya Roy

Keywords: Biodiversity, Ecology, Human Pressures, Gridded data, Africa, sub-Saharan Africa, expert knowledge, place-based

Last updated: 2025-12-03

Projects Br Dwgd (`docs/projects/br_dwgd.md`)

Brazilian Daily Weather Gridded Data(BR-DWGD) 1961-2020

The Comprehensive Brazilian Meteorological Gridded Dataset represents a significant advancement in meteorological research, addressing the growing demand for precise and extensive meteorological data. This dataset builds upon its predecessor by enhancing spatial resolution to 0.1Β° x 0.1Β° and expanding temporal coverage from January 1961 to July 2020. Incorporating elevation and temperature lapse rates, the dataset improves gridded interpolations for minimum and maximum temperatures, while also encompassing other crucial variables such as precipitation, solar radiation, wind speed, and relative humidity.

This dataset derives from a meticulous fusion of data from 11,473 rain gauges and 1,252 weather stations, enabling accurate interpolations. The selection of optimal interpolation methods, determined via ranked cross-validation statistics, underscores the dataset's commitment to precision. With two categories of gridded controls provided, researchers gain tools to assess interpolation accuracy against station data. As a comprehensive resource, the Comprehensive Brazilian Meteorological Gridded Dataset stands poised to catalyze advancements in climate, meteorology, and agricultural studies, offering nuanced insights for multifaceted scientific investigations.

These dataset presents the daily meteorological gridded data set from Brazil (BR-DWGD). The variables are Precipitation (pr, mm); maximum and minimum temperature (Tmax, tmin, Β°C); solar radiation (Rs, MJ/m2); relative humidity (RH, %); wind speed at 2 meters (u2, m/s) and evapotranspiration (ETo, mm). The temporal coverage is 1961/01/01-2020/07/31 (except precipitation: 1961/01/01-2022/12/31) and has the spatial resolution 0.1Β° x 0.1Β°, just for Brazil territory. You can find links to the dataset here

Dataset post processing

The datasets were provided as multiband netcdf files with each representing a single day since 1961 and then partitioned across 20 year intervals. There were then converted and split into single geotiff images and merged so they could be continious collections with about 21,762 images per collection except Precipitation which extends till 2022. The rain gauge and weather station location data was further added to the assets. The datasets must be scaled and offset should be applied to represent true values and they are included in the table below as well as the sample script.

Variable Variable name Units Offset Scale
pr Precipitation mm 225 0.006866665
Eto evapotranspiration mm 0 0.051181102
Tmax maximum temperature C 15 0.001068148
Tmin minimum temperature C 15 0.001068148
RH Relative humidity Percentage 0 0.393700787
RS Solar radiation MJ/m2 0 0.157086614
U2 Wind speed m/s 0 0.059055118

Citation

text
Xavier, A. C., Scanlon, B. R., King, C. W., & Alves, A. I. (2022). New improved Brazilian daily weather gridded data (1961–2020).
International Journal of Climatology, 42( 16), 8390– 8404. https://doi.org/10.1002/joc.7731

Earth Engine Snippet

js
var ET = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/ET");
var PR = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/PR");
var RH = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/RH");
var RS = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/RS");
var TMAX = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/TMAX");
var TMIN = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/TMIN");
var U2 = ee.ImageCollection("projects/sat-io/open-datasets/BR-DWGD/U2");
var RAIN_GAUGES = ee.FeatureCollection("projects/sat-io/open-datasets/BR-DWGD/RAIN_GAUGES");
var WEATHER_STATIONS = ee.FeatureCollection("projects/sat-io/open-datasets/BR-DWGD/WEATHER_STATIONS");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/BR-DWDG-EXAMPLE

License

The datasets are provided under a Attribution 4.0 International (CC BY 4.0) license.

Provided by: Xavier, A. C. et al

Curated in GEE by : Samapriya Roy

Keywords: Brazil, maximum temperature, minimum temperature, precipitation, solar radiation, wind speed, relative humidity, evapotranspiration

Projects Bss (`docs/projects/bss.md`)

Bare Earth’s Surface Spectra 1980-2019

This datasets provides global bare surface area and frequency for a 30 year time range using Landsat Imagery in Google Earth Engine.

From the paper we find

Earth’s surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. Two additional products were obtained with a similar technique: a) Earth’s bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth’s bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth’s total land area and on 95% of land when considering only agricultural areas.

Read the paper here

Use the following credit when these datasets or paper is cited:

text
DemattΓͺ, JosΓ© AM, et al. "Bare earth’s Surface Spectra as a proxy for Soil Resource Monitoring."
Scientific reports 10.1 (2020): 1-11.

Earth Engine Snippet

js
var bare_surface = ee.Image('users/geocis/BareSurfaces/BS_1980_2019');
var bare_frequency = ee.Image('users/geocis/BareSurfaces/BF_1980_2019');

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:geophysical-biological-biogeochemical/BARE_EARTH_SPECTRA

App Website: App link here

Source Code to App: https://code.earthengine.google.com/6b2935468ce30e08ce693a9cc95f943c

Shared License:
This work is licensed under a Creative Commons Attribution 4.0. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Created & Curated by: DemattΓͺ, JosΓ© AM, et al

Keywords: Bare Earth Surface, Soil, Geomorphology, Landsat, Bare Surface Frequency

Last updated: 2021-06-12

Projects C3slc (`docs/projects/c3slc.md`)

Copernicus Climate Change Service (C3S) Land Cover Classification Gridded Maps (1992 -2022)

This dataset from the Copernicus Climate Change Service (C3S) provides global annual land cover (LC) maps from 1992 to the present. The maps describe the land surface using 22 classes defined by the UN Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). This dataset is designed for consistency with the European Space Agency (ESA) Climate Change Initiative (CCI) global annual LC maps from 1992-2015. The product is tailored for climate modelling communities but is also valuable for applications like forest monitoring, land accounting, and desertification studies due to its long-term consistency and yearly updates. You can download the documentation here

Dataset Description

The C3S LC maps are produced at a spatial resolution of approximately 300m (~0.00278Β°). The production methodology ensures temporal consistency across the entire series. A unique 10-year baseline LC map was first generated using the entire archive (2003-2012) of Medium Resolution Imaging Spectrometer (MERIS) data. This baseline is then annually updated (and was initially back-dated) by detecting land cover changes.

Change detection is performed using a variety of satellite time series:

  • Advanced Very-High-Resolution Radiometer (AVHRR) for the period 1992-1999.
  • SPOT-Vegetation (SPOT-VGT) for 1999-2013.
  • PROBA-Vegetation (PROBA-V) for 2014-2019.
  • Sentinel-3 OLCI and SLSTR for 2020 onwards.

In addition to the land cover class layer, the dataset includes four quality flag layers that provide information on the pixel state, processing status, number of observations, and the number of changes detected over the time series.

Available Layers

The dataset includes the main land cover classification layer and several quality flag layers.

Variable Name Description Units
lccs_class Land cover class per pixel, defined using the UN LCCS legend of 22 primary classes. The full legend is available in the product user guide. Dimensionless
processed_flag A flag indicating whether a pixel was processed (1) or not (0). Dimensionless
current_pixel_state Identifies the pixel's status from observations, distinguishing between clear land, water, snow/ice, cloud, and cloud shadow. Dimensionless
observation_count The number of valid observations used to derive the baseline classification for each pixel. Dimensionless
change_count The number of LC changes observed per pixel during the period from 1992 to the latest map year. Dimensionless

Citation

text
Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Bogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., & Arino, O. (2017). Land Cover Climate Change Initiative - Product User Guide v2. Issue 2.0.

Dataset Citation

text
Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on DD-MMM-YYYY)

Earth Engine Snippet

text
/* Detailed source-code truncated for AI context efficiency. */

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/C3S-LC

License

The products are made available to the public by ESA and the consortium. You may use one or several CCI-LC products land cover map for educational and/or scientific purposes, without any fee on the condition that you credit the ESA Climate Change Initiative and in particular its Land Cover project as the source of the CCI-LC database. Should you write any scientific publication on the results of research activities that use one or several CCI-LC products as input, you shall acknowledge the ESA CCI Land Cover project in the text of the publication and provide the project with an electronic copy of the publication ([email protected]). If you wish to use one or several CCI-LC products in advertising or in any commercial promotion, you shall acknowledge the ESA CCI Land Cover project and you must submit the layout to the project for approval beforehand ([email protected]).

Provided by: Copernicus Climate Change Service (C3S) / UCLouvain

Keywords: Land Cover, Climate Change, LCCS, Satellite, Copernicus, C3S, Global, ESA-CCI

Curated in GEE by: GEE Community

Last updated: 2025-04-19

Projects Ca Canopy Ht (`docs/projects/ca_canopy_ht.md`)

Canopy height: forested ecosystems of Canada

This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution β€” one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada β€” the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI.

The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.

You can read the complete paper here and download the dataset at this link

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

text
Sothe, Camile, Alemu Gonsamo, Ricardo B. Lourenço, Werner A. Kurz, and James Snider. "Spatially Continuous Mapping of Forest Canopy Height in Canada
by Combining GEDI and ICESat-2 with PALSAR and Sentinel." Remote Sensing 14, no. 20 (2022): 5158.

Data Citation

text
Sothe, Camile; Gonsamo, Alemu; Snider, James; Lourenço, Ricardo B.; Kurz, Werner A. (2022): Spatially continuous canopy height maps of forested
ecosystems of Canada. 4TU.ResearchData. Dataset. https://doi.org/10.4121/21363081.v1

Earth Engine Snippet: Canopy Height GEDI

js
var gedi_fc_ht = ee.Image("projects/sat-io/open-datasets/CA_FOREST/GEDI_forest_canopy_height_250m_v1");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-TREE-CANOPY-HEIGHT-GEDI

Earth Engine Snippet: ICESat2

js
var icesat_fc_ht = ee.Image("projects/sat-io/open-datasets/CA_FOREST/ICESat2_forest_canopy_height_250m_v1");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-TREE-CANOPY-HEIGHT-ICESAT

License

This dataset is available under a Creative Commons BY-4.0 license

Created by: Sothe,Camile, et al. 2022

Curated in GEE by : Samapriya Roy

Keywords: LiDAR analysis, ICESat-2, GEDI, canopy height distribution, Carbon storage and distribution

Last updated on GEE: 2022-10-20

Projects Ca Fa (`docs/projects/ca_fa.md`)

Landsat-derived forest age for Canada's forested ecosystems (2019)

Landsat-derived forest age for Canada’s forested ecosystems 2019. Satellite-based forest age map for 2019 across Canada’s forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada’s forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an "old tree" category.

Forest area codes:

  • No data: Non treed (Value = 255)
  • 0-150: Forest age in years
  • 151: Old class (forest age greater than 150 years)

Approach used to compute the Landsat-derived forest age for Canada (2019)

Map for displaying the approach followed to compute forest age for the treed areas in Canada’s forested ecosystems for a given year, in this case 2019.

  1. The disturbance approach uses change detection protocols to detect disturbance from 1985 to 2019, with time since disturbance used as a proxy for forest age.
  2. The recovery approach uses Landsat surface reflectance composites to identify pixels exhibiting evidence of recovery from a disturbance that occurred within the twenty years prior to 1985, allowing for the extension of forest age estimates to 1965. Finally,
  3. The allometric approach couples inverted allometric equations with maps of forest structure and productivity metrics to model forest age for those pixels that show no evidence of disturbance or recovery to a maximum mapped age of 150 years.

Forest area codes:
0: Non treed
1: Disturbance approach
2: Recovery approach
3: Allometric approach

See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada’s National Inventory (NFI). Maltman et al. (2023)

Citation

text
Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada’s forested ecosystems.
Remote Sensing of Environment 290, 113529.

Earth Engine Snippet: Landsat-derived forest age for Canada (2019)

js
var age = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_forest_age_2019");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-AGE-2019

Earth Engine Snippet: Approach used to compute the Landsat-derived forest age for Canada (2019)

js
var age_appro = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_forest_age_2019_approach");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-AGE-2019-APPROACH

Download Tool/Code snippets, if any: Download link
https://opendata.nfis.org/downloads/forest_change/CA_forest_age_2019.zip

Required

License Information: Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada)

Created by: Maltman et al. (2023)

Curated in GEE by: Spencer Bronson and Samapriya Roy

Keywords: Forest age, Forest inventory, Land cover, Landsat, Time since disturbance

Last updated: March 15th 2023

Projects Ca Fao (`docs/projects/ca_fao.md`)

Canada Landsat derived FAO forest identification (2019)

Landsat-based forest area consistent with FAO definitions for Canada's forested ecosystems. To conform with international reporting guidelines and programs, using Landsat data we map the forest area for Canada following the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. Annually representative maps were produced using over three decades of annual land cover data generated from Landsat derived time series land cover and change information (to generate a spatially explicit estimate of the forest area of Canada in 2019). We mapped the area of 'forest', as defined by the FAO, for Canada's 650 Mha of forested ecozones. The map includes the current forest cover in a given year (i.e. 2019), plus the satellite-based temporally informed forest area where tree cover had been temporarily lost due to fire or harvest. See Wulder et al. (2020) for an overview of the methods, data, image processing, as well as information on accuracy assessment using Canada’s National Inventory (NFI). You can download the dataset here

Forest area codes for the dataset are

0: Non forest
1: Current forest area 2019
2: Temporally informed forest area 2019

Citation

text
Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change
information to map forest area consistent with national and international reporting requirements. Forestry 93(3), 331-343.

Earth Engine Snippet

js
var ca_fao = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_FAO_forest_2019");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FAO-FOREST-IDENTIFICATION-2019

License

Open Government Licence - Canada

Created by: Wulder et al. (2020)

Curated by: Spencer Bronson and Samapriya Roy

Keywords: Forest area, temporally informed forest area, disturbance informed forest area, Forest inventory, Land cover, Landsat

Last updated: 2023-03-29

Projects Ca Fires (`docs/projects/ca_fires.md`)

Canada 2023 Wildfires

Canada's 2023 wildfire season represented the largest area burned in a single fire season in Canada’s modern history. Using the Tracking Intra- and
Inter-year Change (TIIC) algorithm, wildfires occurring within Canada’s forested ecosystems during the 2023 fire season were detected at a 30-m
resolution. Time series data used to identify wildfires originated from Sentinel-2A and -2B, and Landsat-8 and -9. Fires have been grouped into two
classes based on detection period: summer fires and fall fires. Summer fires were detected between May 30 and September 17, and fall fires were
detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire.

For the fall period, TIIC only detected changes within a 4-km buffer of NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This
approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow
cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada’s forested ecozones, representing
1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires
(Pelletier et al., 2024). You can download the dataset here

Citation

text
Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. https://doi.org/10.1016/j.srs.2024.100145

Earth Engine Snippet

js
var image = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_2023_Summer_Fall");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-2023

License

This work is licensed under and freely available to the public Open Government Licence - Canada.

Created by: Pelletier et al. 2024

Curated in GEE by : Spencer Bronson and Samapriya Roy

keywords: Wildfire, Tracking Intra- Inter-year Change (TIIC), Landsat, Sentinel, Burned Area, Fire Occurrence, Canada

Last updated on GEE: 2024-08-29

Projects Ca Forest Fire (`docs/projects/ca_forest_fire.md`)

Canada Landsat Derived Wildfire disturbance & Magnitude 1985-2020

The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of wildfire in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of wildfire change over Canada’s forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2020 for Canada's 650 Mha forested ecosystems.

Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see ( Hermosilla et al. 2016). The data available is Change year for Wildfire Events. You can download the dataset here

Canada Landsat-Derived Forest Wildfire Change Magnitude dNBR (1985-2020)

Wildfire change magnitude dNBR 1985-2020. Spectral change magnitude for wildfires that occurred from 1985 and 2020 expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. This layer value has been transformed for data storage efficiency. The actual dNBR value can be calculated as follows dNBR = value / 100. Higher dNBR values are related to higher burn severity. You can download the dataset here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Dataset Citation

text
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B., 2016. Mass data processing of time series Landsat imagery:
pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054.

Code Snippet

js
var ca_forest_fire = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Fire_1985-2020");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-1985-2020

js
var ca_forest_fire_mag = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_dNBR_1985_2020");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-MAGNITUDE-1985-2020

License

This work is licensed under and freely available to the public Open Government Licence - Canada.

Created by: Hermosilla et al. 2016

Curated in GEE by : Spencer Bronson and Samapriya Roy

keywords: Forest Fire, Forest inventory, Land cover, Landsat, Machine learning

Last updated on GEE: 2023-07-02

Projects Ca Forest Harvest (`docs/projects/ca_forest_harvest.md`)

Canada Landsat Derived Forest harvest disturbance 1985-2020

The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall
characterization of harvest in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of harvest change over Canada’s
forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to
characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems
from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest
disturbances caused by wildfire and harvest for the period 1985–2020 for Canada's 650 Mha forested ecosystems.

Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven
changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with
lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that
some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the
data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see
(Hermosilla et al. 2016). The data available is Change year for Harvest Events and can be downloaded here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Dataset citation

text
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Campbell, L.B., 2016. Mass data processing of time series Landsat imagery:
pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054.

Code Snippet

js
var ca_forest_harvest = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Harvest_1985-2020");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FOREST-HARVEST-1985-2020

License

This work is licensed under and freely available to the public Open Government Licence - Canada.

Created by: Hermosilla et al. 2016

Curated in GEE by : Samapriya Roy

keywords: Forest Harvest, Forest inventory, Land cover, Landsat, Machine learning

Last updated on GEE: 2023-01-28

Projects Ca Lc (`docs/projects/ca_lc.md`)

High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)

The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2022. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance=weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar).
For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) No. 112780. DOI: https://doi.org/10.1016/j.rse.2021.112780 and Hermosilla et al. (2018) https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719

The data represents annual forest land cover of Canada's forested ecosystems for 1984-2022. An image compositing window of August 1 -30 days was used to generate the best-available-pixel (BAP) image composites used as the source data for land cover classification. The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada's forests, were led by Canadian Forest Service of Natural Resources Canada, partnered with the University of British Columbia, with support from the Canadian Space Agency, augmented by processing capacity from WestGrid of Compute Canada.

Citation

Paper citation

text
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized
implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. No. 112780.
[Hermosilla et al. 2022](https://www.sciencedirect.com/science/article/pii/S0034425721005009)

When using this data, please cite as:

text
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized
implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. No. 112780.
DOI: https://doi.org/10.1016/j.rse.2022.112780 [Open Access]

Class Schema

#686868 Class Code: 0 Unclassified
#3333ff Class Code: 20 Water
#ccffff Class Code: 31 Snow/Ice
#cccccc Class Code: 32 Rock/Rubble
#996633 Class Code: 33 Exposed/Barren Land
#ffccff Class Code: 40 Bryoids
#ffff00 Class Code: 50 Shrubs
#993399 Class Code: 80 Wetland
#9933cc Class Code: 81 Wetland Treed
#ccff33 Class Code: 100 Herbs
#006600 Class Code: 210 Coniferous
#00cc00 Class Code: 220 Broad Leaf
#cc9900 Class Code: 230 Mixedwood

Earth Engine Snippet

js
var forest_lc = ee.ImageCollection("projects/sat-io/open-datasets/CA_FOREST_LC_VLCE2");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FORESTED-ECOSYSTEM-LC

License

This work is licensed under and freely available to the public Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada).

Created by: Hermosilla et al. 2022

Curated in GEE by : Samapriya Roy

keywords: Land cover; Classification; Machine learning; Land cover change; Landsat; Lidar; ICESat-2

Last updated on GEE: 2024-08-29

Projects Ca Sbfi (`docs/projects/ca_sbfi.md`)

Canadian Satellite-Based Forest Inventory (SBFI)

The Satellite-Based Forest Inventory (SBFI) provides information on Canada’s forested land cover, disturbance recovery, structure, species, and stand age in 2020, as well as stand-replacing disturbances from 1985-2020. The SBFI polygons represent homogeneous forest conditions similar to those of stands delineated in a strategic forest inventory. More than 25 million SBFI polygons were delineated using a multiresolution segmentation algorithm applied to the 2020 Landsat surface-reflectance BAP image composite (30-m spatial resolution), fire year, and harvest year layers derived from Landsat using the C2C approach. A minimum map unit of 0.45 ha (5 pixels) was used to define polygons. The entirety of Canada’s forest ecosystems were mapped using the same data, attributes, and temporal representation, resulting in a common vegetation inventory system of Canada’s ~650 Mha forested ecosystems. Given the large and diverse forest area of Canada, the strength of an SBFI lies in the use of a consistent data source and methodology across jurisdictional boundaries, and managed and unmanaged forest areas, enabling consistently generated synoptic, spatially explicit information outputs. The data included herein are based upon free and open satellite data and information products following established and communicated approaches.

Full descriptions of feature attributes are found within the attached data dictionary or within the downloadable dataset found here

??? example "CA SBFI feature attributes"

text
| Group        | Field                              | Description                                                         | Units                |
|--------------|------------------------------------|---------------------------------------------------------------------|----------------------|
| ID           | ID                                 | Unique polygon identifier                                           |                      |
|              | TILE                               | Tile identifier                                                     |                      |
| Geometry     | AREA_HA                            | Area of the polygon                                                 | ha                   |
|              | PERIMETER_M                        | Length of polygon’s boundary                                        | m                    |
| Stratification | JURSDICTION                       | Most represented province/territory                                 |                      |
|              | ECOZONE                            | Most represented terrestrial ecozone as defined by Ecological Stratification Working Group (1996) |                      |
|              | ECOPROVINCE                        | Most represented ecoprovince as defined by Ecological Stratification Working Group (1996) |                      |
|              | ECOREGION                          | Most represented ecoregion as defined by Ecological Stratification Working Group (1996) |                      |
|              | MANAGEMENT                         | Most represented land status from the forest management classification from Stinson et al_ (2019) |                      |
| Land cover   | LC_WATER                           | Area covered by water                                               | % of polygon area    |
|              | LC_SNOW_ICE                        | Area covered by snow/ice                                            | % of polygon area    |
|              | LC_ROCK_RUBBLE                     | Area covered by rock/rubble                                         | % of polygon area    |
|              | LC_EXPOSED_BARREN                  | Area covered by exposed/barren land                                 | % of polygon area    |
|              | LC_BRYOIDS                         | Area covered by bryoids                                             | % of polygon area    |
|              | LC_SHRUBS                          | Area covered by shrubs                                              | % of polygon area    |
|              | LC_WETLAND                         | Area covered by wetland                                             | % of polygon area    |
|              | LC_WETLAND-TREED                   | Area covered by wetland-treed                                       | % of polygon area    |
|              | LC_HERBS                           | Area covered by herbs                                               | % of polygon area    |
|              | LC_CONIFEROUS                      | Area covered by coniferous                                          | % of polygon area    |
|              | LC_BROADLEAF                       | Area covered by broadleaf                                           | % of polygon area    |
|              | LC_MIXEDWOOD                       | Area covered by mixedwood                                           | % of polygon area    |
|              | LC_TREED                           | Area covered by treed vegetation derived from combining the land cover classes | % of polygon area    |
|              | LC_FAO_FOREST                      | Area covered by forest consistent with FAO definitions (Wulder et al_ 2020) | % of polygon area    |
|              | LC_WETLAND_VEGETATION              | Area covered by wetlands derived from combining the land cover classes | % of polygon area    |
| Disturbances | DISTURB_FIRE_PERC                 | Area impacted by fire disturbances                                  | % of polygon area    |
|              | DISTURB_FIRE_YEAR                 | Modal year of fire disturbances                                     | years                |
|              | DISTURB_FIRE_MAGNITUDE_MIN        | Minimum value of fire magnitude                                      | dNBR                 |
|              | DISTURB_FIRE_MAGNITUDE_MAX        | Maximum value of fire magnitude                                      | dNBR                 |
|              | DISTURB_FIRE_MAGNITUDE_AVG        | Average value of fire magnitude                                      | dNBR                 |
|              | DISTURB_FIRE_MAGNITUDE_SD         | Standard deviation of fire magnitude                                | dNBR                 |
|              | DISTURB_FIRE_MAGNITUDE_MED        | Median value of fire magnitude                                       | dNBR                 |
|              | DISTURB_HARVEST_PERC              | Area impacted by harvesting disturbances                            | % of polygon area    |
|              | DISTURB_HARVEST_YEAR              | Modal year of harvesting disturbances                                | years                |
| Recovery     | RECOVERY_FIRE_MIN                 | Minimum value of spectral recovery for fire disturbances             | % of pre-disturbance |
|              | RECOVERY_FIRE_MAX                 | Maximum value of spectral recovery for fire disturbances             | % of pre-disturbance |
|              | RECOVERY_FIRE_AVG                 | Average value of spectral recovery for fire disturbances             | % of pre-disturbance |
|              | RECOVERY_FIRE_SD                  | Standard deviation of spectral recovery for fire disturbances        | % of pre-disturbance |
|              | RECOVERY_FIRE_MED                 | Median value of spectral recovery for fire disturbances              | % of pre-disturbance |
|              | RECOVERY_HARVEST_MIN              | Minimum value of spectral recovery for harvesting disturbances      | % of pre-disturbance |
|              | RECOVERY_HARVEST_MAX              | Maximum value of spectral recovery for harvesting disturbances      | % of pre-disturbance |
|              | RECOVERY_HARVEST_AVG              | Average value of spectral recovery for harvesting disturbances      | % of pre-disturbance |
|              | RECOVERY_HARVEST_SD               | Standard deviation of spectral recovery for harvesting disturbances | % of pre-disturbance |
|              | RECOVERY_HARVEST_MED              | Median value of spectral recovery for harvesting disturbances       | % of pre-disturbance |
| Age          | AGE_MIN                            | Minimum forest age                                                  | years                |
|              | AGE_MAX                            | Maximum forest age                                                  | years                |
|              | AGE_AVG                            | Average forest age                                                  | years                |
|              | AGE_SD                             | Standard deviation of forest age                                    | years                |
|              | AGE_MED                            | Median forest age                                                   | years                |
|              | AGE_0_10, AGE_10_20, AGE_20_30, AGE_30_40, AGE_40_50, AGE_50_60, AGE_60_70, AGE_70_80, AGE_80_90, AGE_90_100, AGE_100_110, AGE_110_120, AGE_120_130, AGE_130_140, AGE_140_150, AGE_GT_150 | Ten-year age class frequency distribution | % of treed area in polygon |
| Forest structure | STRUCTURE_CANOPY_HEIGHT_MIN     | Minimum canopy height                                               | m                    |
|              | STRUCTURE_CANOPY_HEIGHT_MAX        | Maximum canopy height                                               | m                    |
|              | STRUCTURE_CANOPY_HEIGHT_AVG        | Average canopy height                                               | m                    |
|              | STRUCTURE_CANOPY_HEIGHT_SD         | Standard deviation of canopy height                                 | m                    |
|              | STRUCTURE_CANOPY_HEIGHT_MED        | Median canopy height                                                | m                    |
|              | STRUCTURE_CANOPY_COVER_MIN     | Minimum canopy cover                                               | %                    |
|              | STRUCTURE_CANOPY_COVER_MAX        | Maximum canopy cover                                               | %                    |
|              | STRUCTURE_CANOPY_COVER_AVG        | Average canopy cover                                               | %                    |
|              | STRUCTURE_CANOPY_COVER_SD         | Standard deviation of canopy cover                                 | %                    |
|              | STRUCTURE_CANOPY_COVER_MED        | Median canopy cover                                                | %                    |
|              | STRUCTURE_LOREYS_HEIGHT_MIN        | Minimum Lorey’s height                                             | m                    |
|              | STRUCTURE_LOREYS_HEIGHT_MAX        | Maximum Lorey’s height                                             | m                    |
|              | STRUCTURE_LOREYS_HEIGHT_AVG        | Average Lorey’s height                                             | m                    |
|              | STRUCTURE_LOREYS_HEIGHT_SD         | Standard deviation of Lorey’s height                               | m                    |
|              | STRUCTURE_LOREYS_HEIGHT_MED        | Median Lorey’s height                                              | m                    |
|              | STRUCTURE_BASAL_AREA_MIN           | Minimum basal area                                                 | m2 haβˆ’1              |
|              | STRUCTURE_BASAL_AREA_MAX           | Maximum basal area                                                 | m2 haβˆ’1              |
|              | STRUCTURE_BASAL_AREA_AVG           | Average basal area                                                 | m2 haβˆ’1              |
|              | STRUCTURE_BASAL_AREA_SD            | Standard deviation of basal area                                   | m2 haβˆ’1              |
|              | STRUCTURE_BASAL_AREA_MED           | Median basal area                                                  | m2 haβˆ’1              |
|              | STRUCTURE_BASAL_AREA_TOTAL         | Total basal area in polygon                                        | m2                   |
|              | STRUCTURE_AGB_MIN                  | Minimum aboveground biomass                                        | t haβˆ’1               |
|              | STRUCTURE_AGB_MAX                  | Maximum aboveground biomass                                        | t haβˆ’1               |
|              | STRUCTURE_AGB_AVG                  | Average aboveground biomass                                        | t haβˆ’1               |
|              | STRUCTURE_AGB_SD                   | Standard deviation of aboveground biomass                          | t haβˆ’1               |
|              | STRUCTURE_AGB_MED                  | Median aboveground biomass                                         | t haβˆ’1               |
|              | STRUCTURE_AGB_TOTAL                | Total aboveground biomass in polygon                               | t                    |
|              | STRUCTURE_VOLUME_MIN               | Minimum gross stem volume                                          | m3 haβˆ’1              |
|              | STRUCTURE_VOLUME_MAX               | Maximum gross stem volume                                          | m3 haβˆ’1              |
|              | STRUCTURE_VOLUME_AVG               | Average gross stem volume                                          | m3 haβˆ’1              |
|              | STRUCTURE_VOLUME_SD                | Standard deviation of gross stem volume                            | m3 haβˆ’1              |
|              | STRUCTURE_VOLUME_MED               | Median gross stem volume                                           | m3 haβˆ’1              |
|              | STRUCTURE_VOLUME_TOTAL             | Total gross stem volume in polygon                                 | m3                   |
| Tree species | SPECIES_NUMBER                     |                                                                    |                      |
|              | SPECIES_1                          | Name of the 1st most common leading tree species representing a percentage of treed area in polygon >2_5% |                      |
|              | SPECIES_2                          | Name of the 2nd most common leading tree species representing a percentage of treed area in polygon >2_5% |                      |
|              | SPECIES_3                          | Name of the 3rd most common leading tree species representing a percentage of treed area in polygon >2_5% |                      |
|              | SPECIES_4                          | Name of the 4th most common leading tree species representing a percentage of treed area in polygon >2_5% |                      |
|              | SPECIES_5                          | Name of the 5th most common leading tree species representing a percentage of treed area in polygon >2_5% |                      |
|              | SPECIES_1_PERC                     | Area covered by the 1st most common leading tree species            | % of treed area in polygon |
|              | SPECIES_2_PERC                     | Area covered by the 2nd most common leading tree species            | % of treed area in polygon |
|              | SPECIES_3_PERC                     | Area covered by the 3rd most common leading tree species            | % of treed area in polygon |      | SPECIES_4_PERC                     | Area covered by the 4th most common leading tree species            | % of treed area in polygon |
|              | SPECIES_5_PERC                     | Area covered by the 5th most common leading tree species            | % of treed area in polygon |
|              | SPECIES_CONIFEROUS_PERC           | Area covered by coniferous tree species                             | % of treed area in polygon |
|              | SPECIES_CML1                       | Name of the 1st most common tree species based on the class membership likelihood values |                      |
|              | SPECIES_CML2                       | Name of the 2nd most common tree species based on the class membership likelihood values |                      |
|              | SPECIES_CML3                       | Name of the 3rd most common tree species based on the class membership likelihood values |                      |
|              | SPECIES_CML4                       | Name of the 4th most common tree species based on the class membership likelihood values |                      |
|              | SPECIES_CML5                       | Name of the 5th most common tree species based on the class membership likelihood values |                      |
|              | SPECIES_CML1_PERC                  | Distribution of the class membership likelihood values of the 1st most common tree species | % of class membership likelihood from treed pixels in polygon |
|              | SPECIES_CML2_PERC                  | Distribution of the class membership likelihood values of the 2nd most common tree species | % of class membership likelihood from treed pixels in polygon |
|              | SPECIES_CML3_PERC                  | Distribution of the class membership likelihood values of the 3rd most common tree species | % of class membership likelihood from treed pixels in polygon |
|              | SPECIES_CML4_PERC                  | Distribution of the class membership likelihood values of the 4th most common tree species | % of class membership likelihood from treed pixels in polygon |
|              | SPECIES_CML5_PERC                  | Distribution of the class membership likelihood values of the 5th most common tree species | % of class membership likelihood from treed pixels in polygon |
|              | SPECIES_CML_CONIFEROUS_PERC       | Proportion of class membership likelihood values of coniferous tree species | % of class membership likelihood from treed pixels in polygon |
|              | SPECIES_CML_ASSEMBLAGES           | Name of the tree species conforming an assemblage                   |                      |
|              | SPECIES_CML_ASSEMBLAGES_PERC      | Proportion of class membership likelihood values conforming the assemblage | % of class membership likelihood from treed pixels in polygon |
| Symbology    | SYMB_LAND_BASE_LEVEL              | Land base level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) |                      |
|              | SYMB_LAND_COVER_LEVEL             | Land cover level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) |                      |
|              | SYMB_VEGETATION_LEVEL             | Vegetation level classification based on the NFI land cover hierarchy (Wulder et al_ 2008) |                      |
|              | SYMB_DISTURBANCE                  | Simplified coding for disturbance type and year                      |                      |
|              | SYMB_RECOVERY                     | Simplified coding for spectral recovery                             |                      |
|              | SYMB_AGE                          | Simplified coding for forest age                                    |                      |

Dataset postprocessing

The tile datasets are merged into a single feature collection for ease of use. The grid file is kept as is for users to understand how the grids are created.

Citation

text
Wulder, Michael A., Txomin Hermosilla, Joanne C. White, Christopher W. Bater, Geordie Hobart, and Spencer C. Bronson. "Development and
implementation of a stand-level satellite-based forest inventory for Canada." Forestry: An International Journal of Forest Research (2024): cpad065.

Dataset Citation

text
Wulder, M.A., Hermosilla, T., White, J.C., Bater, C.W., Hobart, G., Bronson, S.C., 2024. Development and implementation of a stand-level
satellite-based forest inventory for Canada. Forestry: An International Journal of Forest Research. https://doi.org/10.1093/forestry/cpad065

Earth Engine Snippet

js
var sbfi_merged = ee.FeatureCollection("projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/CA_SBFI_MERGED");
var grid_fe = ee.FeatureCollection("projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/GRID_forested_ecosystems");
var grid_labels = ee.FeatureCollection("projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/Grid_Labels");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SBFI

License

This work is licensed under and freely available to the public under the Open Government Licence - Canada.

Created by: Wulder et al. 2024

Curated in GEE by : Samapriya Roy

Keyworks: Landsat, land cover, change detection, forest structure, biomass; NFI

Last updated in GEE: 2024-08-29

Projects Ca Species (`docs/projects/ca_species.md`)

High Resolution Tree Species Information for Canada

Distance-to-second class for the leading tree species map

Distance-to-second class (D2SC) value used as indicator of attribution confidence for the leading tree species map produced from surface reflectance values in a spatially exhaustive, 30-m spatial resolution, Landsat image composite representing year 2019 conditions. Also included in the modeling of species are geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada??s National Forest Inventory using a 150x150 km tiling system. D2SC is computed using the class membership probabilities derived from the first and second most voted classes from the Random Forests models.

High Resolution Maps of tree species membership likelihood

Tree species maps indicate the class membership probability of all possible classes on a pixel level. The maps are generated using a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Values represent the class membership probabilities derived from the Random Forests votes. Regional classification models were generated based on Canada??s National Forest Inventory (NFI) using a 150x150 km tiling system. The regional classification models utilize and aim to map only the trees species known to be present in a given tiling unit based on the information provided by the NFI.

High Resolution Map of leading tree species distribution

Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (xxxx). Regional classification models were generated based on Canada??s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).

For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276

Citation

text
Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada's forested ecosystems. Remote Sensing of Environment 282, 113276.

Earth Engine Snippet: Distance to Second Class

js
var D2SC = ee.Image("projects/sat-io/open-datasets/CA_FOREST/DISTANCE2SECOND");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-DISTANCE-2-SECOND-CLASS

Earth Engine Snippet: tree species membership likelihood

js
var membership_likelihood_prob = ee.ImageCollection("projects/sat-io/open-datasets/CA_FOREST/SPECIES_CLASS_MEMBERSHIP_PROBABILITIES");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SPECIES-CLASS-MEM-PROBABILITIES

Earth Engine Snippet: leading tree species

js
var lead_tree_species = ee.Image("projects/sat-io/open-datasets/CA_FOREST/LEAD_TREE_SPECIES");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-LEAD-TREE-SPECIES

License

This work is licensed under and freely available to the public Open Government Licence - Canada.

Created by: Hermosilla et al. 2022

Curated in GEE by : Samapriya Roy

keywords: Tree species, Forest inventory, Land cover, Landsat, Machine learning, Classification

Last updated on GEE: 2022-10-11

Projects Ca Species Ts (`docs/projects/ca_species_ts.md`)

Canada Long Term Tree Species (1984-2022)

In this dataset, we share maps of annual dominant tree species (also known as leading tree species) from 1984-2022 covering the entirety of Canada’s 650 Mha forested ecosystems using Landsat time-series imagery at a 30-m spatial resolution. Classifications are based on regionally representative Random Forests model using local training samples from Canada’s National Forest Inventory (Hermosilla et al., 2022). Descriptive metrics provide information on spectral, geographic, climatic, and topographic characteristics. Initial annual tree species classifications were subjected to a time series post-classification process using the forward-backward Hidden Markov Model to improve the temporal consistency of tree species transitions within the time series. Assessment of the annual species maps using independent validation data resulted in an overall accuracy of 86.1% Β± 0.14% (95%-confidence interval). These data allow consistent comparison of trends and rates of change in tree species composition nationally and across regions using a common time frame, spatial resolution, and analytical approach.

Citation

text
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Bater, C.W., Hobart, G.W., 2024. Characterizing long-term tree species dynamics in Canada's forested ecosystems using annual time series remote sensing data. Forest Ecology and Management 572, 122313. https://doi.org/10.1016/j.foreco.2024.122313 (Hermosilla et al. 2024)

You can download the files here, found under the title: Annual Tree Species 1984-2022 and Species_Names here

Dataset Post processing

The datasets were provided as an earth engine folder with images and have been converted to an imagecollection and start and end date have been added to each image in the image collection for filtering.

Earth Engine Snippet

js
var ca_species_ts = ee.ImageCollection("projects/sat-io/open-datasets/CA_FOREST/SPECIES-1984-2022");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SPECIES-TIME-SERIES

License

This work is licensed under and freely available to the public Open Government License - Canada.

Created by: Hermosilla et al. 2024

Curated in GEE by : Spencer Bronson & Samapriya Roy

Keywords: Landsat, Time series analysis, Land cover, Land cover change, Forest succession, Dominant species

Last updated :2024-10-15

Projects Caml (`docs/projects/caml.md`)

Cyanobacteria Aggregated Manual Labels (CAML)

Continuous monitoring for cyanobacteria blooms in small, inland water bodies via in-situ sampling and analysis can be challenging not only due to the number and locations of
water bodies to cover, but also due to the dynamic nature of algal growth and toxin production. Detection targets vary with cyanobacteria strains as well as physical, chemical,
and biological factors. Ground monitoring also lacks consistency as sampling methods, frequency, and analytical techniques vary from region to region. However, remote sensing
allows systematic data collection over a large area to identify regions with potential harmful algal growth. We introduce the Cyanobacteria Aggregated Manual Labels (CAML), a
large dataset of in-situ cyanobacteria measurements for investigations of cyanobacteria detection and severity classification in inland water bodies across the United States.
Relevant satellite imagery from publicly available endpoints are applicable to use when applying the CAML dataset to models.

The dataset labels ground measurements of cyanobacteria cell counts at 23,570 points in U.S. inland water bodies over 2013 - 2021. Algorithms trained on this data could be used to estimate cyanobacteria cell counts in water bodies for timely water quality and public health interventions and to gain an understanding of environmental and anthropogenic factors associated with cyanobacteria
incidence and proliferation. Data is provided in a comma-separated values (CSV) format. You can find the dataset here

Severity levels are based on World Health Organization (WHO) cyanobacteria density thresholds.

  • Low: 0 - 20,000 cells/ml
  • Moderate: 20,000 - 100,000 cells/ml
  • High: > 100,000 cells/ml

However users should feel free to to use their own thresholds as makes sense for their needs.

Dataset Citation

text
S. Gupta, E. Gelbart, R. Gupta, K. Wetstone, and E. Dorne (2024). Cyanobacteria Aggregated Manual Labels Dataset (NASA and DrivenData). SeaBASS. http://dx.doi.org/10.5067/SeaBASS/CAML/DATA001

Earth Engine Snippet

js
var caml = ee.FeatureCollection("projects/sat-io/open-datasets/HAB-DETECTION/CAML_cyanobacteria_abundance_20211229_R1");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/CYANOBACTERIA-AGG-MANUAL-LABELS

License

Following the NASA Earth Science Data and Information Policy, all SeaBASS data are publicly available.

Provided by: NASA, UC Berkeley,DrivenData, ASRC Federal

Curated in GEE by: Samapriya Roy

Keywords: water quality, HAB, Cyanobacteria, Manual Labels, Ground data

Last updated: 2024-03-20

Projects Can Drought Outlook (`docs/projects/can_drought_outlook.md`)

Canadian Drought Outlook

The Canadian Drought Outlook raster dataset is produced by the Agriculture and Agri-Food Canada (AAFC). The Canadian Drought Outlook predicts whether drought across Canada will emerge, stay the same or get better over the target month. In calculating the outlook, consideration is given to Agroclimate indices, such as the Standard Precipitation Index (SPI), the Standard Precipitation Evaporation Index (SPEI), and the Palmer Drought Severity Index (PDSI). The drought outlook is issued on the first Thursday of each calendar month and is valid for 32 days from that date. You can get additional information about this dataset here and on the climate engine org dataset page here.

Dataset Description

Categorical Values

Value Interpretation
-9999 NoData Value
0 No data
1 Drought removal
2 Drought improves
3 Drought develops
4 Drought persists
5 Drought worsens

Spatial Information

Parameter Value
Spatial extent Canada
Spatial resolution ~0.8-km (1/100-deg)
Temporal resolution Monthly
Time span 2021-06-01 to present
Update frequency Updated first week of each month

Variables

Variable Details
Drought category ('drought_outlook_class') - Units: Drought outlook classification
- Scale factor: 1.0

Citation

text
Agriculture and Agri-Food Canada, 2021, "Canadian Drought Outlook", Agroclimate, Geomatics and Earth Observation Division, Science and Technology
Branch.

Earth Engine Snippet

js
// Read in Image Collection and get single image
var cdo_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-aafc-cdo-monthly')
var cdo_i = cdo_ic.first()

// Print image to see bands
print(cdo_i)

// Visualize a single image
var cdo_palette = ["#ffffff", "#4a7733", "#dfb73d", "#b6a083", "#775412", "#c24d1b"]
Map.addLayer(cdo_i, {min:0, max:4, palette: cdo_palette}, 'cdo_i')

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/CANADA-DROUGHT-OUTLOOK

License

Data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. The terms of this Agreement govern your use and reproduction of the data instead of the copyright reproduction statements found in Important Notices on the Agriculture and Agri-Food Canada website.

Keywords: drought, Canada, forecast, AAFC

Provided by: Agriculture and Agri-Food Canada (AAFC)

Curated in GEE by: Climate Engine Org

Projects Canopy (`docs/projects/canopy.md`)

ETH Global Sentinel-2 10m Canopy Height (2020)

The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical satellite images like Sentinel-2 offer dense observations globally, but cannot directly measure vertical structures. By fusing GEDI with Sentinel-2, we have developed a probabilistic deep learning model to retrieve canopy height from Sentinel-2 images anywhere on Earth, and to quantify the uncertainty in these estimates.

The presented approach reduces the saturation effect commonly encountered when estimating canopy height from satellite images, allowing to resolve tall canopies with likely high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Such data play an important role for conservation, e.g., we find that only 34% of these tall canopies are located within protected areas. Our model enables consistent, uncertainty-informed worldwide mapping and supports an ongoing monitoring to detect change and inform decision making. The approach can serve ongoing efforts in forest conservation, and has the potential to foster advances in climate, carbon, and biodiversity modelling. You can download the cloud optimized geotiffs here

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Citation

text
Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner. "A high-resolution canopy height model of the Earth." arXiv preprint arXiv:2204.08322 (2022).

Earth Engine Snippet

js
var canopy_height = ee.Image("users/nlang/ETH_GlobalCanopyHeight_2020_10m_v1");
var standard_deviation = ee.Image("users/nlang/ETH_GlobalCanopyHeightSD_2020_10m_v1");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-10m-CANOPY-HEIGHT

GEE app link: https://nlang.users.earthengine.app/view/global-canopy-height-2020

GEE app source code link: https://code.earthengine.google.com/fefca6457efb90c0a3f8ae9806bee792

License

The ETH Global Canopy Height 2020 product is provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.

Created by: Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner

Curated by: Samapriya Roy

Keywords: Sentinel-2, Forest, Canopy Height, Machine Learning, CNN

Last updated on GEE: 2022-03-29

Projects Carbon Projects (`docs/projects/carbon_projects.md`)

Carbon Offset Project Boundaries

Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (n=433, 75.3%) as well as manual georeferencing and digitization (n=127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 +/- 0.015).

You can read the dataset publication here and find the dataset here.

Database notes

  • Users must carefully distinguish between overall project areas and project accounting areas based on their specific analysis requirements:

    • The project area is defined as the geographical area where the project participants implement activities to reduce deforestation.
    • The project accounting area is defined as the geographical area of the project that was used to calculate carbon credit issuance.

  • This database does not represent a census of nature-based carbon projects and does not contain all varieties of nature-based carbon projects.

  • Users should verify that any georeferencing inaccuracies will not significantly impact their analyses.

  • The boundaries included in the database reflect the data available in the registries at the time of access, with some projects regularly updating their information.

  • We note that we were unable to assess the accuracy of the boundaries constructed from linear features or from a developer provided protocol.

Dataset Description

Attribute Description
Project Name The name of the carbon project as given in the documentation.
ProjectID A combination of the registry abbreviation and project number.
Registry Name The name of the registry where project information is hosted. Possible values are: American Carbon Registry, BioCarbon Registry, Climate Action Reserve, EcoRegistry, Gold Standard, Verra.
Methodology The name of the methodology used for the project’s implementation.
Project Type The type of forestry carbon offset program. One of the following: ARR, AD, IFM.
Country The country where the project is located.
Project Developer Name Name of the entity or individual organizing, proposing, or advocating a particular carbon offset project. In case of multiple entities/individuals, only the first name is recorded here.
Project Start Date Date of the start of the crediting period (mm/dd/yyyy).
Project End Date Date of the end of the crediting period (mm/dd/yyyy).
Date of Entry Date when the project information was added to the database (mm/dd/yyyy).
Processing Approach One of four possible values: Official, Georeferenced, Linear, or Method. Official if the canonical boundary was obtained from a registry or the project developer. Georeferenced if the boundary data was obtained via georeferencing and digitizing maps obtained from the project documents. Linear if the boundary data were distributed as linear features that were processed into geometries. Method if a developer specified protocol was followed to obtain the boundary data.
PD Declined to Provide One of: Yes, No, N/A. Yes if the project developer was contacted for the project geometry but declined to provide the necessary information. No if the project developer provided the geometries or sufficient information to create the project geometry. N/A if the geometry was available from a registry and the developer was not contacted.
Geometry Type One of: Point or Polygon. Indicates if the geometry in the database is a point or a polygon.
Project Area The well-known text (WKT) representation of the geographical area where the project participants implement activities to reduce deforestation.
Project Accounting Area The WKT representation of the geographical area of the project which was used to calculate carbon credits. If the project area is the same as the project accounting area, then the project accounting area is not defined.
Project Reference Region The well-known text (WKT) representation of the geographical area of the project from where historical and current deforestation and forest degradation quantities and trends are obtained. This is not always defined.
Comment Notes about how manual referencing was carried out or other relevant information.

Citation

text
Karnik, Akshata, John Kilbride, Tristan Goodbody, Rachael Ross, and Elias Ayrey. "An open-access database of nature-based carbon offset project
boundarie." (2024).

Dataset Citation

text
Karnik, A., Kilbride, J., Goodbody, T., Rachel, R., & Ayrey, E. (2024). A global database of nature-based carbon offset project boundaries [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11459391

Earth Engine Snippet

js
var carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL');

var visParams = {
    palette: ['#9ab555'],
    min: 0.0,
    max: 1550000.0,
    opacity: 0.8,
};
var carbonoffsets = ee.Image().float().paint(carbonoffsetscol, 'REP_AREA');

Map.addLayer(carbonoffsets, visParams, 'Existing carbon projects area');

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-CARBON-OFFSET-PROJECTS

License

This dataset is made available under Creative Commons Attribution 4.0 International license.

Keywords: carbon, carbon offsets, NBS, climate change, Nature-based climate solutions, Carbon offsets, Geospatial boundaries, Georeferencing,Forest carbon

Curated in GEE by: Filipe Silveiran and Samapriya Roy

Last updated : 2024-09-07

Projects Cc (`docs/projects/cc.md`)

Chesapeake Bay High Resolution Land Cover Dataset (2013-2014)

This raster dataset was developed as part of the Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service funded through an interagency agreement with the Environmental Protection Agency. Virginia Geographic Information Network (VGIN) coordinated with Worldview Solutions the creation of a separate VA statewide high-resolution land cover dataset, which has unique class names and descriptions. For the purposes of a matching bay-wide dataset, this VA dataset was reclassified and some classes were edited to better match the Chesapeake Bay Dataset class definitions below.

High Resolution mapping was used to develop consistent and extremely accurate land cover dataset for all the counties that comprise the Chesapeake Bay watershed. This land cover was created based on 2014 National Agriculture Imagery Program (NAIP) aerial imagery.

Dataset created and developed by the Conservation Innovation Center at the Chesapeake Conservancy. The creation of this dataset was made possible as a result of a cooperative agreement between the Chesapeake Conservancy and the National Park Service being funded through an interagency agreement with the Environmental Protection Agency. This dataset will enhance the ability to guide the most efficient use of resources in the Bay as well as aid the identification of priorities for conservation and restoration. Created based on 2013/2014 National Agriculture Imagery Program (NAIP) aerial imagery. You can find additional information here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

text
Conservation Innovation Center at the Chesapeake Conservancy. Chesapeake Bay High Resolution Land Cover Dataset (2013-2014). Accessed [Month
Year] at https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/

Earth Engine Snippet

js
var chesapeake = ee.Image("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/HRLC/Baywide_13Class_20132014");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CHESEPEAKE_BAY_2013-2014

License

The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. The organizations responsible for generating and funding this dataset make no representations of any kind including, but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors.

Produced by: Conservation Innovation Center at the Chesapeake Conservancy

Curated in GEE by: Samapriya Roy

Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, Chesapeake Bay

Last updated on GEE: 2022-06-12

Projects Ccap Lc (`docs/projects/ccap_lc.md`)

C-CAP High-Resolution Land Cover

The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. The timeframe for this metadata is summer 2016. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.

These detailed products bring NOAA’s national land cover mapping framework to the local level and are developed for specific project-based geographies (not the entire coastal land cover mapping boundary). Data are often developed in partnership with state and local groups. Attributes for this product are as follows: 0 Background, 1 Unclassified (Cloud, Shadow, etc), 2 Impervious, 3 4 5 Developed Open Space, 6 Cultivated Land, 7 Pasture/Hay, 8 Grassland, 9 Deciduous Forest, 10 Evergreen Forest, 11 Mixed Forest, 12 Scrub/Shrub, 13 Palustrine Forested Wetland, 14 Palustrine Scrub/Shrub Wetland, 15 Palustrine Emergent Wetland, 16 Estuarine Forested Wetland, 17 Estuarine Scrub/Shrub Wetland, 18 Estuarine Emergent Wetland, 19 Unconsolidated Shore, 20 Bare Land, 21 Open Water, 22 Palustrine Aquatic Bed, 23 Estuarine Aquatic Bed, 24 Tundra, 25 Snow/Ice, Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database.

This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. You can read a sample metadata file here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

text
National Oceanic and Atmospheric Administration, Office for Coastal Management. β€œName of Data Set.” Coastal Change Analysis Program (C-
CAP) High-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/
htdata/raster1/landcover/bulkdownload/hires/.

Preprocessing

The regional land cover dataset files were downloaded for each region. If the files were img then they were convert to GeoTIFF. Each region was converted into a collection and start and end dates were added based on available information and filenames.

Earth Engine Snippet

text
/* Detailed source-code truncated for AI context efficiency. */

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-HRLC-HI

License

The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.

Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)

Curated in GEE by: Samapriya Roy

Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA, NOAA

Last updated on GEE: 2022-06-12

Projects Ccap Mlc (`docs/projects/ccap_mlc.md`)

C-CAP Medium-Resolution Land Cover Beta

The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.

These data should be considered to be BETA-level or draft products. They are based on 1-meter land cover mapping that were entirely automated and the relationship of those data to existing wetlands data. As such, there may be issues that result from the different vintages of these products, as well as the errors included in each. While not perfect, the data should provide an example of what level of detail would be possible through such higher-resolution mapping. These data are not jurisdictional or intended for use in litigation. NOAA does not assume liability for any damages or misrepresentations caused by inaccuracies in the data, or as a result of the data used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

These detailed products bring NOAA’s national land cover mapping framework to the local level and are developed for specific project-based geographies (not the entire coastal land cover mapping boundary). Data are often developed in partnership with state and local groups. Attributes for this product are as follows: 0 Background, 1 Unclassified (Cloud, Shadow, etc), 2 Impervious, 3 4 5 Developed Open Space, 6 Cultivated Land, 7 Pasture/Hay, 8 Grassland, 9 Deciduous Forest, 10 Evergreen Forest, 11 Mixed Forest, 12 Scrub/Shrub, 13 Palustrine Forested Wetland, 14 Palustrine Scrub/Shrub Wetland, 15 Palustrine Emergent Wetland, 16 Estuarine Forested Wetland, 17 Estuarine Scrub/Shrub Wetland, 18 Estuarine Emergent Wetland, 19 Unconsolidated Shore, 20 Bare Land, 21 Open Water, 22 Palustrine Aquatic Bed, 23 Estuarine Aquatic Bed, 24 Tundra, 25 Snow/Ice, Recommended Citation. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database.

This dataset was created by NOAA's Ocean Service, Office for Coastal Management (OCM). Random Forest Classification: The initial 1m spatial resolution 6 class high resolution land cover product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. You can read a sample metadata file here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

text
National Oceanic and Atmospheric Administration, Office for Coastal Management. β€œName of Data Set.” Coastal Change Analysis Program (C-
CAP) 10m-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/
htdata/raster1/landcover/bulkdownload/hires/.
text
National Oceanic and Atmospheric Administration, Office for Coastal Management. β€œName of Data Set.” Coastal Change Analysis Program (C-
CAP) 30m-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed [Month Year] at www.coast.noaa.gov/
htdata/raster1/landcover/bulkdownload/hires/.

Preprocessing

The regional land cover dataset files were downloaded for each region. If the files were img then they were convert to GeoTIFF. Each region was converted into a collection and start and end dates were added based on available information and filenames.

Earth Engine Snippet

js
var CCAP_LC10 = ee.ImageCollection("projects/sat-io/open-datasets/NOAA/ccap_10m");
var CCAP_LC30 = ee.ImageCollection("projects/sat-io/open-datasets/NOAA/ccap_30m");

Sample Code LC: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-LC-BETA

js
var CCAP_IMP30 = ee.ImageCollection("projects/sat-io/open-datasets/NOAA/ccap_30m_impervious");

Sample Code Impervious: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-IMPERVIOUS

License

The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.

Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)

Curated in GEE by: Samapriya Roy

Keywords: Land Use, Land Cover, Urban Watch, Remote Sensing, High Resolution, OBIA, NOAA

Last updated on GEE: 2022-06-12

Projects Ccap Wpotential (`docs/projects/ccap_wpotential.md`)

C-CAP Wetland Potential 30m

The probability rating which covers landcover mapping provides a continuum of wetness from dry to water. The layer is not a wetland classification but provides the wetland likelihood at a specific location. The rating was developed through a modelling process combining multiple GIS and remote sensing data sets including soil characteristics, elevation, existing wetland inventories, hydrographical extents and satellite imagery . Data can be downloaded here. This classification is based on GIS and remote sensing data sets with variable ranges from the 1977 to 2010.

This dataset was created by NOAA's Ocean Service, Office for Coastal Management Initial Classification: 1m spatial resolution land cover data developed by the Chesapeake Bay Conservancy, University of Vermont Spatial Analysis Laboratory, and The Virginia Geographic Information Network (VGIN) was the starting point for this dataset. This product was developed using a Geographic Object-Based Image Analysis (GEOBIA) processing framework applied to NAIP imagery and Lidar data. This involves taking each image to be classified and grouping the pixels based on spectral and spatial properties into regions of homogeneity called objects. The resulting objects are the primary units for analysis. The original dataset can be downloaded here:https://chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/.

The resulting 1-meter land cover was then resampled to a 10-meter raster. This was done using a 10x10 focal pixel window to compute the percent value associated with either the impervious or tree classes, for each land cover type in the 100 pixel neighborhood around each target pixel, and subsequently performing a nearest neighbor resampling of the resulting values. The output values were then coded to the appropriate percentage (between 0 and 100). This 10-meter raster was resampled a second time, using the average of values within a 3x3 focal pixel window in order to obtain the appropriate values over each 30-meter pixel area. Output values between 0 and 100 represent the appropriate percentage mapped within each pixel. Class 127 identifies areas not included in this mapping, or no data areas.

Class values

0: This is the value for nodata.
1: The value indicates there is an extremely low likelihood of wetness.
2-9: The value indicates a likelihood of wetness, where 1 is very unlikely and 9 is highly likely.
10: The value indicates open water.

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

text
National Oceanic and Atmospheric Administration, Office for Coastal Management. β€œName of Data Set.” Coastal Change Analysis Program (C-
CAP) Wetland Potential Layer: NOAA Office for Coastal Management. Accessed [Month Year] at https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/wetlandpotential/.

Earth Engine Snippet

js
var ccap_wetland_potential = ee.Image("projects/sat-io/open-datasets/NOAA/conus_ccap_wetland_potential");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCAP-WETLAND-POTENTIAL

License

The dataset is released under an assumed CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.

Produced by: NOAA's Ocean Service, Office for Coastal Management (OCM)

Curated in GEE by: Samapriya Roy

Keywords: Wetland, Coastal data, NOAA, Remote Sensing

Last updated on GEE: 2022-05-17

Projects Cci Agb (`docs/projects/cci_agb.md`)

ESA CCI Global Forest Above Ground Biomass

This dataset updated to v6.0 provides estimates of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022. These estimates are derived from a combination of Earth observation data, depending on the year, obtained from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument, and JAXA's Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from other Earth observation sources. The dataset has been generated as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) program by the Biomass CCI team.

Compared to version 5, version 6 consists of updated AGB maps for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and new AGB maps for 2007 and 2022. The dataset includes multi-temporal observations at L-band for all biomes and for each year. The above-ground biomass (AGB) maps utilize revised allometries, which are now based on a more extensive collection of spaceborne LiDAR data from the GEDI and ICESat-2 missions. The retrieval algorithm incorporates extended ICESat-2 observations to calibrate retrieval models and a refined cost function that preserves temporal features as expressed in the remote sensing data, limiting biases between the 2007-2010 and the 2015+ maps.

The data products consist of two (2) global layers that include estimates of:

  1. Above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots

  2. Per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)

Additionally provided in this version release are aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).

Also included are the change products which include these layers:

  1. Standard deviation of AGB change: Maps showing the uncertainty of AGB change for consecutive years (e.g., 2016-2015, 2017-2016, 2018-2017, 2019-2018, 2020-2019, 2021-2020, 2022-2021), over a decade (2020-2010), and over the interval 2010-2007.

  2. Quality flag of AGB change: Maps indicating the reliability of AGB change estimates for the same time periods mentioned above.

Note: AGB change values can be calculated by differencing the corresponding AGB maps. Data are provided in both netcdf and geotiff format.

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Dataset preprocessing for GEE

  • Above ground biomass files were downloaded for each years as tiles and then merged into single year images rasters. Common metadata properties were preserved so as to be passed over to the collection
  • The same approach for a yearly composite was applied to the Standard deviations tiles as well to make sure they can be mapped on top of each other.
  • For ingestion a manifest file was created with Band 1 being AGB and Band 2 being SD so we could reduce the overall files for an image collection. The ingestion used a no data value of 0.
  • For change product Band 1 was AGB_DIFF_SD and Band 2 was AGB_DIFF_QF and these were to represent standard deviation of AGB biomass change and quality flag of AGB biomass change.
  • Metadata properties were copied over from AGB with some slight changes to fields and the dates were preprocessed and updated to v6.0.

Citation

text
Santoro, M.; Cartus, O. (2025): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018,
2019, 2020, 2021 and 2022, v6.0. NERC EDS Centre for Environmental Data Analysis, 17 April 2025. doi:10.5285/95913ffb6467447ca72c4e9d8cf30501.

Earth Engine Snippet

js
var agb = ee.ImageCollection("projects/sat-io/open-datasets/ESA/ESA_CCI_AGB");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ESA-CCI-ABOVEGROUND-BIOMASS

js
var agb_change = ee.ImageCollection("projects/sat-io/open-datasets/ESA/ESA_CCI_AGB_DIFF");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/ESA-CCI-ABOVEGROUND-BIOMASS-DIFFERENCE

License and Access

Use of these data is covered by the license information found here. The CCI BIOMASS datasets have been processed by the CCI BIOMASS consortium led by the University
of Aberystwyth (U.K.). They are made available to the public by ESA and the consortium. When using these data you must cite them correctly using the citation given on the catalogue record. The dataset is under a public access with access to these data available to both registered and non-registered users

Created by: CCI BIOMASS consortium led by the University of Aberystwyth (U.K.)

Curated in GEE by: Samapriya Roy

Keywords: satellite observation, forest, biomass

Created: 2025-04-17

Last updated in GEE: 2025-09-01

Changelog

v6.0 (April 2025)

  • Extended temporal coverage: Added new AGB maps for 2007 and 2022
  • Updated existing maps: Refreshed AGB estimates for 2010, 2015, 2016, 2017, 2018, 2019, 2020, and 2021
  • Enhanced calibration: Extended ICESat-2 observations used for retrieval model calibration
  • Improved temporal consistency: Refined cost function to limit biases between 2007-2010 and 2015+ maps
  • New aggregated products: Added coarser resolution data products (1, 10, 25, and 50km)
  • Extended change products: New AGB change maps for additional consecutive years (2022-2021) and 2010-2007 interval
  • Multiple formats: Data now available in both netcdf and geotiff formats
  • Updated citation: New DOI and publication details

v5.01 (August 2024)

  • Initial release with AGB estimates for 2010, 2015, 2016, 2017, 2018, 2019, 2020, and 2021
  • Utilized revised allometries based on GEDI and ICESat-2 spaceborne LiDAR data
  • Incorporated temporal information to capture biomass dynamics
  • Provided AGB change products for consecutive years and decade (2010-2020)
  • Band 1: AGB, Band 2: Standard deviation for main collection
  • Band 1: AGB_DIFF_SD, Band 2: AGB_DIFF_QF for change collection

Projects Cci Lc (`docs/projects/cci_lc.md`)

CCI LAND COVER S2 PROTOTYPE LAND COVER 20M MAP OF AFRICA 2016

The CCI Land Cover (LC) S2 prototype is a high resolution LC map at 20m over Africa based on 1 year of Sentinel-2A observations from December 2015 to December 2016. The main objective of the 'S2 prototype LC map at 20m of Africa 2016' release was to collect users feedback for further improvements. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.

The legend of the S2 prototype LC 20m map of Africa 2016 was built after reviewing various existing typologies (e.g. LCCS, LCML…), global (e.g. GLC-share, GlobeLand30) and national experiences (Africover, SERVIR-RMCD). The legend includes 10 generic classes that appropriately describe the land surface at 20m: "trees cover areas", "shrubs cover areas", "grassland", "cropland", "vegetation aquatic or regularly flooded", "lichen and mosses / sparse vegetation", "bare areas", "built up areas", "snow and/or ice" and "open water".

Among the Land Cover classes, two of them were largely identified thanks to external dataset: the "open water" class was based on the Global Surface Water product from JRC/EC and the "urban areas" relied both on the Global Human Settlement Layer from JRC/EC and on the Global Urban Footprint from DLR. Two classification algorithms, the Random Forest (RF) and Machine Learning (ML), were chosen to transform the cloud-free reflectance composites generated by the pre-processing module into a land cover map. The two maps resulting from both approaches are then combined either to select the best representation of a land cover class which will be part of the final S2 prototype LC 20m map of Africa 2016 or, in case of unreliable LC class delineation, the reference layer is used to consolidate the land cover classification.

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Earth Engine snippet

js
var esa_cci = ee.Image("projects/sat-io/open-datasets/ESA/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v10");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/CCI-LC-20M-AFRICA

License and Terms of Use

The present product is made available to the public by ESA and the consortium. You may use S2 prototype LC 20m map of Africa 2016 for educational and/or scientific purposes, without any fee on the condition that you credit the ESA Climate Change Initiative and in particular its Land Cover project as the source of the CCI-LC database:

text
Copyright notice:
Β© Contains modified Copernicus data (2015/2016)
Β© ESA Climate Change Initiative - Land Cover project 2017

By downloading the prototype product you recognize that this prototype is not a final product and you are aware of the consequences of using a prototype that has not been validated. By downloading the prototype product, you also commit to fill the User Feedback Form (see below). Should you write any scientific publication on the results of research activities that use one or several CCI-LC products as input, you shall acknowledge the ESA CCI Land Cover project in the text of the publication and provide the project with an electronic copy of the publication ([email protected]).

If you wish to use S2 prototype LC 20m map of Africa 2016 in advertising or in any commercial promotion, you shall acknowledge the ESA CCI Land Cover project and you must submit the layout to the project for approval beforehand ([email protected]).

Created by: ESA

Curated in GEE by : Samapriya Roy

keywords: Landcover, Sentinel-2 Landcover, ESA, Africa Landcover

Last updated on GEE: 2023-02-27

Projects Cems Fire (`docs/projects/cems_fire.md`)

CEMS Fire Danger Indices

Fire danger indices from the ECMWF, calculated using weather forecasts from historical simulations provided by ECMWF ERA5 reanalysis.The CEMS Fire Danger Indices dataset provides a comprehensive set of indices designed to assess and quantify fire danger and wildfire risk at regional and global scales. You can get additional information on the dataset here and on the climateengine.org dataset page here

  • Build-up Index: The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes.

  • Burning Index: The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same.

  • Drought Code: The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800.

  • Drought Factor: The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum

  • Duff Moisture Code: The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150.

  • Energy Release Component: The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative.

  • Fine Fuel Moisture Code: The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96.

  • Fire Daily Severity Rating: Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value.

  • Fire Danger Index: The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation

  • Fire Weather Index:The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions.

  • Ignition Component: The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions.

  • Initial Fire Spread Index: The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1.

  • Keetch-Byram Drought Index: The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation.

  • Spread Component: The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit.

Spatial Extent Global
Spatial Resolution ~25km (0.25 deg)
Temporal Resolution Daily
Time Span 1940-01-01 to Present
Update Frequency Updated daily with one week lag
Variables
Build-up Index ('build_up_index') - Units: Dimensionless
- Scale Factor: 1.0
Burning Index ('burning_index') - Units: Dimensionless
- Scale Factor: 1.0
Drought Code ('drought_code') - Units: Dimensionless
- Scale Factor: 1.0
Drought Factor ('drought_factor') - Units: Dimensionless
- Scale Factor: 1.0
Duff Moisture Code ('duff_moisture_code') - Units: Dimensionless
- Scale Factor: 1.0
Energy Release Component ('energy_release_component') - Units: J/m2
- Scale Factor: 1.0
Fine Fuel Moisture Code ('fine_fuel_moisture_code') - Units: Dimensionless
- Scale Factor: 1.0
Fire Daily Severity Rating ('fire_daily_severity_rating') - Units: Dimensionless
- Scale Factor: 1.0
Fire Danger Index ('fire_danger_index') - Units: Dimensionless
- Scale Factor: 1.0
Fire Weather Index ('fire_weather_index') - Units: Dimensionless
- Scale Factor: 1.0
Ignition Component ('ignition_component') - Units: %
- Scale Factor: 1.0
Initial Fire Spread Index ('initial_fire_spread_index') - Units: Dimensionless
- Scale Factor: 1.0
Keetch-Byram Drought Index ('keetch_byram_drought_index') - Units: Dimensionless
- Scale Factor: 1.0
Spread Component ('spread_component') - Units: Dimensionless
- Scale Factor: 1.0

Citation

text
Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., LibertΓ‘, G., & Krzeminski, B. (2020). ERA5-based global
meteorological wildfire danger maps. Scientific data, 7(1), 1-11. 'Contains modified Copernicus Climate Change Service information [Year]'

Copernicus Climate Change Service, Climate Data Store, (2019): Fire danger indices historical data from the Copernicus Emergency Management
Service. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.0e89c522 (Accessed on DD-MMM-YYYY)

Earth Engine Snippet

js
// Read in Image Collection and get most recent image
var cems_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-cems-fire-daily-4-1')
var cems_i = cems_ic.filterDate('2023-01-01', ee.Date(Date.now())).sort('system:time_start', false).first()

// Print first image to see bands
print(cems_i)

// Visualize select bands from first image - additional variables are available in the Image Collection
var fire_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse()
Map.addLayer(cems_i.select('burning_index'), {min: 0, max: 50, palette: fire_palette}, 'burning_index')
Map.addLayer(cems_i.select('fire_weather_index'), {min: 0, max: 50, palette: fire_palette}, 'fire_weather_index')
Map.addLayer(cems_i.select('fire_danger_index'), {min: 0, max: 50, palette: fire_palette}, 'fire_danger_index')
Map.addLayer(cems_i.select('ignition_component'), {min: 0, max: 50, palette: fire_palette}, 'ignition_component')
Map.addLayer(cems_i.select('spread_component'), {min: 0, max: 10, palette: fire_palette}, 'spread_component')
Map.addLayer(cems_i.select('energy_release_component'), {min: 0, max: 50, palette: fire_palette}, 'energy_release_component')
Map.addLayer(cems_i.select('fire_daily_severity_rating'), {min: 0, max: 50, palette: fire_palette}, 'fire_daily_severity_rating')

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CEMS-FIRE-DAILY

License

The license for CEMS Fire Danger Indices data is the Copernicus Licence to Use Copernicus Products (the "Licence"). The Licence is a modified Creative Commons Attribution 4.0 International (CC BY 4.0) license, with the following additional terms:

  • You must acknowledge the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF) as the source of the data.
  • You must not use the data for commercial purposes without prior permission from the European Commission.
  • You must not modify the data in a way that could mislead the public about its source or accuracy.

Data are subject to the License to Use Copernicus Products found here

Keywords: ECMWF, Copernicus, wildfire, climate, reanalysis, ERA5, daily, near real-time

Provided by : Copernicus

Curated in GEE by: ClimateEngine Org

Projects Cflux (`docs/projects/cflux.md`)

Global Forest Carbon Fluxes (2001-2024)

Net forest carbon flux represents the net exchange of carbon between forests and the atmosphere between 2001 and 2024, calculated as the balance between carbon emitted by forests and removed by (or sequestered by) forests during the model period (megagrams CO2 emissions/ha). Net carbon flux is calculated by subtracting average annual gross removals from average annual gross emissions in each modeled pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of carbon between 2001 and 2024. Net fluxes are calculated following IPCC Guidelines for national greenhouse gas inventories in each pixel where forests existed in 2000 or were established between 2000 and 2020 according to the Global Forest Change tree cover change data of Hansen et al. (2013) and Potapov et al. (2022). This layer reflects the cumulative net flux during the model period (2001-2024) and must be divided by 24 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model.

Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2024. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2020 according to the Global Forest Change tree cover loss data of Hansen et al. (2013) and tree cover gain data of Potapov et al. (2022). Carbon removed by each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2024) and must be divided by 24 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model.

Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2 emissions/ha, between 2001 and 2023). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil) and greenhouse gases (CO2, CH4, N2O). Emissions estimates for each pixel are calculated following IPCC Guidelines for national greenhouse gas inventories where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of Hansen et al. (2013). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance. Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion forest carbon removals layer. The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data.

All three layers are part of the forest carbon flux model described in Harris et al. (2021) and updated in Gibbs et al. (2025). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist governments and non-government actors with tracking greenhouse gas fluxes from forests and decreasing emissions or increasing removals by forests. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Please also find the dataset on Global Forest Watch.

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Dataset updates

Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. Since 2023, multiple model input data sets and constants have been changed to improve accuracy and incorporate the latest scientific advances. Please refer to this blog post for detailed information on all updates.

Citation

text
Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021).
https://doi.org/10.1038/s41558-020-00976-6
text
var emissions = ee.Image("projects/sat-io/open-datasets/forest_carbon_fluxes/gross_emissions");
var removals = ee.Image("projects/sat-io/open-datasets/forest_carbon_fluxes/gross_removals");
var net_flux = ee.Image("projects/sat-io/open-datasets/forest_carbon_fluxes/net_flux");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/GLOBAL-FOREST-CARBON-FLUXES

License

The Global Forest Carbon Fluxes (2001-2024) products are provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.

Created by: Harris, N.L., Gibbs, D.A., Baccini, A. et al

Curated in GEE by: David Gibbs, Melissa Rose, Nancy Harris and Samapriya Roy

Keywords: Carbon emissions, forest change, climate, carbon, greenhouse gas, IPCC, removals, sequestration

Last updated on GEE: 2025-09-28

Changelog

Version 1.4.2 (2025)

Major updates discussed in the GFW blog post

Model Updates:

  • Updated to year 2024 tree cover loss data
  • Fire emission combustion factors: Updated from 2006 IPCC Guidelines to 2019 Refinement values for boreal and temperate forests. This significantly reduces combustion factors in temperate forests where fires are associated with natural disturbances or unknown drivers
  • Soil carbon emission factors: Updated stock-change factors for mineral soil with revised IPCC values. Factors are now higher for tropical forests when forests are converted to permanent agriculture
  • Emissions from mineralization of soil nitrogen: Added Nβ‚‚O emissions from soil nitrogen mineralization following IPCC guidelines where driver of tree cover loss is permanent agriculture, shifting cultivation, hard commodity production, or settlement and infrastructure
  • Higher resolution drivers of tree cover loss: Updated from 10-km Curtis et al. 2018 map to 1-km Sims et al. 2025 map, including additional drivers at smaller spatial scales (natural disturbances other than wildfires, hard commodities like mining)

Projects Chirps Prelim (`docs/projects/chirps_prelim.md`)

Climate Hazards Group InfraRed Precipitation with Station Data-Prelim (CHIRPS-Prelim)

The Climate Hazards Center InfraRed Precipitation With Station Data-Prelim (CHIRPS-Prelim) is a blend of CHIRPS data with in situ precipitation data
to unbias the data and enhance its accuracy. The process of generating CHIRPS- Prelim is similar to the CHIRPS process, with the main difference
being its reliance on Global Telecommunication System (GTS) stations only, which are available in near-real time. Blending of CHIRP with GTS-only
stations allows for the latency of CHIRPS- Prelim to be <5 days. Note that, in general, the differences in CHIRPS-Prelim and CHIRPS are within
acceptable limits, as both data sets share the same climatological mean. You can find additional information here and on climate org dataset page here.

This dataset is to be used in conjunction with CHIRPS Pentad/Daily collections, which are Earth Engine assets at:

  • UCSB-CHG/CHIRPS/PENTAD

  • UCSB-CHG/CHIRPS/DAILY

Dataset Description

Spatial Information

Parameter Value
Spatial extent Global
Spatial resolution 4.8-km grid (1/20 deg)
Temporal resolution 5-day (pentad)
Time span 2015 to present
Update frequency Updated weekly

Variables

Variable Details
Precipitation ('precipitation') - Units: Millimeters
- Scale factor: 1.0

Citation

text
Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.
P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p.,
http://dx.doi.org/10.3133/ds832

Earth Engine Snippet

js
// Read in Image Collections and get single image
var chirps_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-chirps-prelim-pentad')
var chirps_i = chirps_ic.first()

// Print single image to see bands
print(chirps_i)

// Visualize precipitation for single image
var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"]
Map.addLayer(chirps_i.select('precipitation'), {min: 0, max: 200, palette: prec_palette}, 'precipitation')

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CHIRPS-PRELIM

License

This datasets are in the public domain. To the extent possible under law, Pete Peterson has waived all copyright and related or neighboring rights to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS).

Keywords: precipitation, near real-time, climate, CHIRPS

Provided by: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS)

Curated in GEE by: Climate Engine org

Projects Cisi (`docs/projects/cisi.md`)

Harmonized Global Critical infrastructure & Index (CISI)

Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI.

In this study the users generated: (1) a harmonized detailed geospatial data of the world’s main CI systems into a single geospatial database; and (2) a Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The datasets are generated from Open Streetmap extract from 8th January 2021 using https://planet.openstreetmap.org/. You can read the full paper here. You can download the spatial extracts for both the feature type and the Critical Infrastructure Spatial Index (CISI) here

Disclaimer: Whole or parts of the dataset description was provided by the author(s) or their works.

Paper citation

text
Nirandjan, S., Koks, E.E., Ward, P.J. et al. A spatially-explicit harmonized global dataset of critical infrastructure. Sci Data 9, 150 (2022).
https://doi.org/10.1038/s41597-022-01218-4

Dataset citation

text
Nirandjan, Sadhana, Koks, Elco E., Ward, Philip J., & Aerts, Jeroen C.J.H. (2021). A spatially-explicit harmonized global dataset of critical infrastructure (v1.0.0.)
[Data set]. Zenodo. https://doi.org/10.5281/zenodo.4957647

Earth Engine Snippet

js
var global_CISI = ee.Image("projects/sat-io/open-datasets/CISI/global_CISI");
var infrastructure = ee.ImageCollection("projects/sat-io/open-datasets/CISI/amount_infrastructure");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/CRITICAL-INF-SPATIAL-INDEX(CISI)

License

This work is licensed under a Creative Commons Attribution 4.0 International license.

Produced by : Nirandjan, S., Koks, E.E., Ward, P.J. et al

Curated in GEE by : Samapriya Roy

Keywords: : Development indicator, global spatial data, gridded data, critical infrastructure, spatial index

Last updated on GEE: 2022-05-12

Projects Climate Trace (`docs/projects/climate_trace.md`)

Climate Trace Global Emissions Data

Climate TRACE is a non-profit coalition that has unveiled an open emissions database containing more than 352 million assets. The database provides a comprehensive accounting of greenhouse gas (GHG) emissions based primarily on direct, independent observation. It includes every country and territory in the world and covers various emitting activities such as energy production, industrial processes, and land use. The data are derived from satellites, remote sensing, and other public and commercial sources, making it the most comprehensive and granular dataset of recent GHG emissions ever created. The inventory allows for transparent assessment of each country's progress toward emission reduction goals.

For more detailed information, you can visit the Climate TRACE website at climatetrace.org.

Dataset preprocessing

The downloaded datasets were processed to get system:time_start and system:time_end in epoch time and these were added to the data column in GEE. Each sector and their associated emissions dataset sources were processed providing 38,731,650 total features. Not all sectors had emissions locations and some were provided only at a country level.

Citation

text
Climate TRACE - Tracking Realtime Atmospheric Carbon Emissions (2022), Climate TRACE Emissions Inventory,
https://climatetrace.org [Date Accessed].

For individual sectors refer to citation information from the downloads page.

Metadata Descriptors

??? example "Expand to show data attributes and definitions for the emissions database"

text
| Data-attribute            | Definition                                                                                                        |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| source_id                 | The internal Climate TRACE identifier for each individual source of emissions.                                    |
| source_name               | Name of the entity or source that produced the emissions.                                                         |
| source_type               | Description of the emission source classification.                                                                |
| iso3_country              | Corresponds to the ISO 3166-1 alpha-3 specification of the country where the entity is physically located.        |
| original_inventory_sector | Intergovernmental Panel on Climate Change (IPCC) emissions sector to which the emissions source belongs.          |
| start_time                | The time using Coordinated Universal Time (UTC) of emissions, either as an instance of start time of observation. |
| end_time                  | The time using Coordinated Universal Time (UTC) of emissions, either as an instance of end time of observation.   |
| lat                       | Approximate latitude location of the source.                                                                      |
| lon                       | Approximate longitude location of the source.                                                                     |
| geometry_ref              | Corresponds to the reference id to the geopackage file present in the downloads.                                  |
| gas                       | Greenhouse gases for which emissions are reported in metric tonnes.                                               |
| emissions_quantity        | Quantity of gas emitted in metric tonnes.                                                                         |
| temporal_granularity      | Resolution of the data available.                                                                                 |
| activity                  | Activity of the entity producing the emissions, not including units.                                              |
| activity_units            | Units of reported "activity".                                                                                     |
| emissions_factor          | Emissions factor of reported activity.                                                                            |
| emissions_factor_units    | Units of reported "emissions factor" field.                                                                       |
| capacity                  | Capacity of the entity producing emissions, not including units.                                                  |
| capacity_units            | Units of reported "capacity" field.                                                                               |
| capacity_factor           | Corresponds to the ratio of the actual source output (activity) to the source capacity.                           |
| capacity_factor_units     | Units of repored "capacity_factor" field.                                                                         |
| other1                    | Additional data field available for the sub-sector.                                                               |
| other1_def                | Definition of reported data of Other1 field.                                                                      |
| other2                    | Additional data field available for the sub-sector.                                                               |
| other2_def                | Definition of reported data of Other2 field.                                                                      |
| other3                    | Additional data field available for the sub-sector.                                                               |
| other3_def                | Definition of reported data of Other3 field.                                                                      |
| other4                    | Additional data field available for the sub-sector.                                                               |
| other4_def                | Definition of reported data of Other4 field.                                                                      |
| other5                    | Additional data field available for the sub-sector.                                                               |
| other5_def                | Definition of reported data of Other5 field.                                                                      |
| other6                    | Additional data field available for the sub-sector.                                                               |
| other6_def                | Definition of reported data of Other6 field.                                                                      |
| other7                    | Additional data field available for the sub-sector.                                                               |
| other7_def                | Definition of reported data of Other7 field.                                                                      |
| other8                    | Additional data field available for the sub-sector.                                                               |
| other8_def                | Definition of reported data of Other8 field.                                                                      |
| other9                    | Additional data field available for the sub-sector.                                                               |
| other9_def                | Definition of reported data of Other9 field.                                                                      |
| other10                   | Additional data field available for the sub-sector.                                                               |
| other10_def               | Definition of reported data of Other10 field.                                                                     |
| created_date              | Date emissions source was added to the Climate TRACE database.                                                    |
| modified_date             | Last date on which any updates were made to the dataset for the specific source.                                  |

Earth Engine Snippet

text
/* Detailed source-code truncated for AI context efficiency. */

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/CLIMATE-TRACE-EMISSIONS

License

All Climate TRACE data is freely available under the Creative Commons Attribution 4.0 International Public License, unless otherwise noted below

Created by: Climate Trace

Curated in GEE by: Samapriya Roy

Keywords: Climate Trace, Emissions, Sectors, Agriculture, Buildings, Fossil Fuel Operations, Forestry And Land Use, Manufacturing, Mineral Extraction, Power, Transportation, Waste

Last updated: 2024-01-17

Projects Cloudsen12 (`docs/projects/cloudsen12.md`)

CloudSEN12 Global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs) that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms. Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates that match one of the following cloud cover groups:

  • clear (0%)

  • low-cloudy (1% - 25%)

  • almost clear (25% - 45%)

  • mid-cloudy (45% - 65%)

  • cloudy (65% >)

The dataset is available here. For more details check out the website and you can read the preprint of the paper here

Data Citation

text
Aybar, C. et al. CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.
Science Data Bank https://doi.org/10.57760/sciencedb.06669 (2022).

Paper Citation

text
Aybar, C., Ysuhuaylas, L., Loja, J. et al. CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.
Sci Data 9, 782 (2022). https://doi.org/10.1038/s41597-022-01878-2

Currently included layers are:

Earth Engine Snippet: Hand-crafted labels - high-quality

js
var cs12_high = ee.ImageCollection("projects/sat-io/open-datasets/cloudsen12/high");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-HIGH-QUALITY

Earth Engine Snippet: Hand-crafted labels - scribble

js
var cs12_scribble = ee.ImageCollection("projects/sat-io/open-datasets/cloudsen12/scribble");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-SCRIBBLE-QUALITY

Earth Engine Snippet: Hand-crafted labels - nolabel

js
var cs12_nolabel = ee.ImageCollection("projects/sat-io/open-datasets/cloudsen12/nolabel");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-NO-LABEL

Earth Engine Snippet: IPs footprint

text
var cs12_geom = ee.ImageCollection("projects/sat-io/open-datasets/cloudsen12/footprint");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/CloudSEN12-FOOTPRINT

License

This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Curated in GEE by: Samapriya Roy

Keywords: cloud, deep learning, Sentinel-2, Sentinel-1, U-Net

Last updated: 2022-09-18

Projects Cmapper (`docs/projects/cmapper.md`)

Carbon Mapper Data Portal Methane Emissions

???+ note

text
**This dataset is currently only available to those in the [insiders program](https://gee-community-catalog.org/insiders/)**

The Carbon Mapper data portal focused on collecting individual high emission methane point sources. The Carbon mapper portal provides methane plume imagery with emission rates and uncertainties from strong point sources as observed from NASA’s next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and ASU’s Global Airborne Observatory (GAO) airborne platforms.

As per the portal, these systems have near-identical capabilities and serve as prototypes for future sustained global monitoring. The plume concentration maps are available at spatial resolutions ranging from 3 to 8 meters (depending on aircraft altitude), allowing for precise attribution to individual emission sources.The API end point was used to query the overall area over North America yielding 8327 total sites.

You can use the download button too to download the curated zipped plume and geotiff data if you are inclined to use those. You can read their FAQ here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Citation

As per the portal the following papers were used to generate the portal and the user should use the appropriate ones as deemed fit

text
/* Detailed source-code truncated for AI context efficiency. */

Data preprocessing

Based on the Plume extracts that were also available from the data portal certain assumptions and metadata fields were renamed. The campaign-id field was used a plume-id field for all of these observation. While there is a separate plume-id field in the original metadata it seems to be missing for some observations and as such the use of campaign id. This was also to keep alignment with the way in which the plume extracts were created by the data portal itself. Flux rate and Flux uncertainity were also renamed to emission and emission uncertainity.

The plume geotiffs were provided as RGB for rendering to a fixed hex code and color palette, so the metadata was attached to each of these raster geotiff in the collection. The portal also provided the RGB underlying imagery and that too was ingested in a separate collection with the same metadata for easy join. Finally the results were exported into a table, all downloaded for S3 URLs were automated using a custom script I wrote and included in the metadata.

Earth Engine Snippet

js
var plume_geotiffs = ee.ImageCollection("projects/sat-io/open-datasets/carbon-mapper/plume_geo");
var rgb_geotiffs = ee.ImageCollection("projects/sat-io/open-datasets/carbon-mapper/rgb_geo");
var plume_features = ee.FeatureCollection("projects/sat-io/open-datasets/carbon-mapper/plume_feature");

Sample Script: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/CARBON-MAPPER-METHANE-EMISSIONS

License

Carbon Mapper data is provided for non-commercial purposes subject to the Modified Creative Commons Attribution ShareAlike 4.0 International Public License ("CC License"). All third-party use of the data is subject to the CC license at all times. The license details includes terms for Non commercial use and share alike clauses and you can read through the modified terms.

Provided by: Carbon Mapper, Inc.

Curated in GEE by : Samapriya Roy

keywords: Methane Emissions, AVIRIS-NG, Global Airborne Observatory, Plume Emissions , Carbon Mapper Data Portal

Last updated on GEE: 2023-04-16

Projects Colombia Eq 2026 (`docs/projects/colombia_eq_2026.md`)

Colombia Earthquake (Choco) 2026

On 10 August 2026 at 07:34 local time (12:34 UTC), a Mw 7.4 earthquake struck western Colombia, with an epicenter about 5 to 20 km south of San Jose del Palmar in Choco department (USGS puts it at Mw 7.4 and roughly 110 km depth, while the Global CMT project and the Colombian Geological Survey both report Mw 7.5). The rupture was an intermediate depth strike slip event within the subducting Nazca plate, and strong shaking (MMI VI to VII) was felt across a wide swath of western and central Colombia, reaching Cali, Pereira, Manizales, Armenia and Quibdo, as well as parts of Ecuador and Panama. Two independent commercial disaster response programs, Vantor's (formerly Maxar) open data program and Planet Labs' disaster data program, tasked satellite collections over the affected departments, and both are catalogued here as separate Earth Engine collections.

Event Summary

Field Value
Date / time 10 August 2026, 07:34 local (12:34 UTC)
Magnitude Mw 7.4 (USGS), Mw 7.5 (Global CMT, Colombian Geological Survey)
Depth approximately 110 km, strike slip, subducting Nazca plate
Epicenter 5 to 20 km south of San Jose del Palmar, Choco, Colombia (approximately 4.844 N, 76.242 W)
Deaths at least 294 (consolidated figures as of mid August 2026)
Injured / missing 3,900+ injured, 300+ still missing
Affected departments Choco, Valle del Cauca, Risaralda, Quindio, Caldas, Antioquia, plus damage reported as far as Cali
Economic damage estimates diverge widely, from roughly USD 1 to 2 billion (Oxford Economics) up to about USD 10 billion in other reporting, so no single figure is authoritative here

Casualty and damage figures for an event this recent continue to move as recovery operations proceed. Check the sources listed below before quoting a number.

Vantor (Maxar) Open Data

Vantor's open data program released a pre-event baseline collection over the earthquake affected region, 19 images across 2 ingestion runs, all captured before the event (earliest scene 16 July 2026, no post-event imagery in this collection as of ingestion). Sensors are WorldView-2, WorldView-3, WorldView Legion (three separate satellites in the constellation) and GeoEye-1, at native resolutions from 0.35 to 3.26 m. Assets are full-strip cloud optimized GeoTIFFs with a visual band and a thumbnail per acquisition.

js
// Vantor (Maxar) open data, pre-event baseline imagery.
var vantorColombiaEq2026 = ee.ImageCollection("projects/sat-io/open-datasets/VANTOR-DISASTER-DATA/COLOMBIA_EQ_2026");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/VANTOR-OPEN-DATA

Planet Labs Disaster Data

Planet Labs' disaster data program released 157 images across 6 ingestion runs, combining a pre-event basemap with two distinct post-event collection phases. The pre-event baseline is drawn from Planet's Q2 2026 global quarterly basemap (1 April to 1 July 2026, roughly 4.8 m resolution). Post-event coverage was collected the same day and the following day as the earthquake: 57 PlanetScope scenes (approximately 3 m, collected the afternoon of 10 August 2026) spanning the cordillera from Risaralda south to Cauca, and skySat collects (approximately 0.66 m) over Quibdo, the Choco departmental capital, on 10 and 11 August 2026. Sensors are PlanetScope and SkySat, at resolutions from 0.66 to 4.78 m.

js
// Planet Labs disaster data, pre-event basemap plus two post-event phases.
var planetColombiaEq2026 = ee.ImageCollection("projects/sat-io/open-datasets/PLANET-DISASTER-DATA/COLOMBIA_EQ_2026");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-events-layers/PLANET-DISASTER-DATA

License

Both collections on this page are released under Creative Commons Attribution Non Commercial 4.0.

Data provided by: Vantor (Maxar) Open Data Program, Planet Labs PBC Disaster Data

Curated in GEE by: Samapriya Roy

Keywords: disaster, earthquake, Colombia, Choco, Vantor, Maxar, Planet Labs, PlanetScope, SkySat, WorldView, GeoEye

Last updated on GEE: 2026-08-17

Projects Cpc Morph (`docs/projects/cpc_morph.md`)

Climate Prediction Center (CPC) Morphing Technique (MORPH)

The Satellite Precipitation - CMORPH Climate Data Record (CDR) consists of satellite precipitation estimates that have been bias corrected and
reprocessed using the Climate Prediction Center (CPC) Morphing Technique (MORPH) to form a global, high resolution precipitation analysis at a 25-km
(1/2-deg x 1/2-deg) spatial resolution updated daily from 1980-present. Data is reprocessed on a global grid with daily temporal resolution. You can
get additional information here or on climate engine org page here.

Dataset Description

Spatial Information

Parameter Value
Spatial extent Global
Spatial resolution 25-km (1/2-deg x 1/2-deg)
Temporal resolution Daily
Time span 1998-01-01 to present
Update frequency Updated daily with 2-day lag

Variables

Variable Details
Precipitation ('precip') - Units: Millimeters
- Scale factor: 1.0

Citation

text
Xie, Pingping; Joyce, Robert; Wu, Shaorong; Yoo, S.-H.; Yarosh, Yelena; Sun, Fengying; Lin, Roger, NOAA CDR Program (2019): NOAA Climate Data Record
(CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [indicate subset].
NOAA National Centers for Environmental Information.

Earth Engine Snippet

js
// Read in Image Collections and get single image
var cmorph_ic = ee.ImageCollection('projects/climate-engine-pro/assets/noaa-cpc-cmorph/daily')
var cmorph_i = cmorph_ic.first()

// Print single image to see bands
print(cmorph_i)

// Visualize precipitation for single image
var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"]
Map.addLayer(cmorph_i.select('precip'), {min: 0, max: 200, palette: prec_palette}, 'precip')

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/CPC-MORPH

License

NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.

Keywords: NOAA, global, precipitation, climate, near real-time, NWS, CPC, MORPH, CMORPH

Dataset provided by: NOAA

Dataset curatd in GEE by: Climate Engine Org

Projects Csa (`docs/projects/csa.md`)

CropSuite v1.0 Crop Suitability Assessment for Africa

CropSuiteβ€―v1.0 is an open‑source tool that delivers high‑resolution crop‑suitability assessments for 48 species across the African continent, covering both rainfed and irrigated systems. The accompanying dataset, built for the historical window 1991‑2010, contains detailed suitability maps, climate suitability layers, identified limiting factors, estimates of multi‑cropping potential, and the optimal sowing windows for each crop. A central contribution of this work is the explicit incorporation of climate variability into the suitability calculations, offering a sharper picture for climate‑impact studies and agricultural decision‑making.

The modelling framework adopts a fuzzy‑logic formulation grounded in Liebig’s law of the minimum. By employing membership functions, it captures how each crop’s requirements intersect with a suite of climatic, edaphic, and topographic variables. With a spatial resolution of 30β€―arc‑secondsβ€”roughly one kilometreβ€”the model covers a broad portfolio of staples and cash crops, many of which have historically been overlooked in regional assessments, such as cassava, yam, millet, sorghum, cowpea, groundnuts, cocoa, coffee, oil palm, rubber, and banana.

Analyses of the resulting maps reveal that, across Africa, 5.7β€―millionβ€―kmΒ² are highly suitable for cultivation, 10.6β€―millionβ€―kmΒ² fall into a moderate suitability class, 3.3β€―millionβ€―kmΒ² are marginal, and 10.4β€―millionβ€―kmΒ² are deemed unsuitable. The zone between 10Β°β€―N and 10Β°β€―S offers a strong opportunity for multiple cropping, potentially supporting two or three harvests each year. In terms of sheer area, safflower leads with 16.82β€―millionβ€―kmΒ² of suitable land, followed by sesame, guava, cowpea, and mango, which record 15.76, 14.15, 13.61, and 13.39β€―millionβ€―kmΒ² respectively. When the model accounts for the dampening effect of climate variability, the overall area of suitable land shrinks by more than 2.2β€―millionβ€―kmΒ², a reduction that is particularly pronounced in Somalia, Kenya, Ethiopia, South Africa, and the Maghreb countries of Morocco, Algeria, Tunisia, and Libya.

Dataset Description

The CropSuite v1.0 dataset provides comprehensive crop suitability information at 30 arcsec spatial resolution (approximately 1 km at the Equator) for Africa. The dataset uses:

  • Climate Data: CHIRPS v2.0 daily precipitation and CHIRTS v1.0 daily temperature (1991-2010)
  • Soil Data: ISRIC SoilGrids (250m-1000m), WISE database, and ISRIC Global Soil Salinity Map
  • Terrain Data: SRTM elevation data
  • Irrigation: Current irrigated areas based on Meier et al. (2018)

The model accounts for:

  • Climate variability and recurrence rate of potential crop failure
  • Irrigated and rainfed agricultural systems
  • Vernalization requirements for winter crops
  • Lethal temperature thresholds
  • Photoperiodic sensitivity
  • Multiple cropping potential
  • Optimal sowing and harvest dates

The data is organized into 10 thematic collections, with each collection containing 6 scenarios representing different water conditions (rainfed, irrigated, combined) and climate variability considerations (with/without).

Crops Included (48 Total)

Alfalfa, Arabica Coffee, Avocado, Banana, Barley, Beans, Cabbage, Carrot, Cashew, Cassava, Castor Bean, Chickpea, Citrus, Cocoa, Coconut, Cotton, Cowpea, Green Pepper, Groundnut, Guava, Maize, Mango, Millet, Oil Palm, Olive, Onion, Papaya, Pea, Pineapple, Potato, Rapeseed, Rice, Robusta Coffee, Rubber tree, Rye, Safflower, Sesame, Sorghum, Soy, Sugar Cane, Sunflower, Sweet Potato, Tea, Tobacco, Tomato, Watermelon, Wheat, Yam

Available Collections and Layers

The dataset is organized into 10 thematic ImageCollections, each containing 6 scenario images:

Collections:

  1. climate_suitability - Climate suitability index for each crop
  2. climate_suitability_mc - Climate suitability for multiple cropping systems
  3. crop_limiting_factor - Primary limiting factor for crop growth
  4. crop_suitability - Overall crop suitability considering all factors
  5. multiple_cropping - Potential number of harvests per year
  6. optimal_sowing_date - Optimal day of year to plant crop
  7. optimal_sowing_date_mc_first - Optimal sowing date for first harvest in multiple cropping
  8. optimal_sowing_date_mc_second - Optimal sowing date for second harvest in multiple cropping
  9. optimal_sowing_date_mc_third - Optimal sowing date for third harvest in multiple cropping
  10. suitable_sowing_days - Number of suitable days per year for sowing

Scenarios in each collection:

  • historical_1991-2010_ir_novar - Irrigated without climate variability
  • historical_1991-2010_ir_var - Irrigated with climate variability
  • historical_1991-2010_rf_novar - Rainfed without climate variability
  • historical_1991-2010_rf_var - Rainfed with climate variability
  • historical_1991-2010_rfir_novar - Combined rainfed/irrigated without climate variability
  • historical_1991-2010_rfir_var - Combined rainfed/irrigated with climate variability

Band Structure:

Each scenario image contains up to 48 bands, one per crop. Band names correspond to crop names (e.g., maize, wheat, cassava).

Collection Value Range Data Type Pyramiding Policy
crop_suitability 0-100 Continuous MEAN
climate_suitability 0-100 Continuous MEAN
climate_suitability_mc 0-100 Continuous MEAN
suitable_sowing_days 0-365 Continuous MEAN
optimal_sowing_date 1-365 Discrete MODE
optimal_sowing_date_mc_first 1-365 Discrete MODE
optimal_sowing_date_mc_second 1-365 Discrete MODE
optimal_sowing_date_mc_third 1-365 Discrete MODE
multiple_cropping 1-3 Discrete MODE
crop_limiting_factor Categorical Categorical MODE

Citation

text
Zabel, F., KnΓΌttel, M., & Poschlod, B. (2025). CropSuite v1.0 – a comprehensive open-source
crop suitability model considering climate variability for climate impact assessment.
Geosci. Model Dev., 18, 1067–1087. https://doi.org/10.5194/gmd-18-1067-2025

Dataset Citation

text
Zabel, F., KnΓΌttel, M., & Poschlod, B. (2024). CropSuite – Crop suitability assessment
for 48 crops under rainfed and irrigated conditions for Africa (1.0) [Data set].
Zenodo. https://doi.org/10.5281/zenodo.14514729

Earth Engine Snippet

text
/* Detailed source-code truncated for AI context efficiency. */

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CROPSUITE-AFRICA

App: Check out a quick app for all crops

License

This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users are free to share and adapt the material for any purpose, provided appropriate credit is given to the source.

Provided by: Zabel et al 2025, University of Basel

Curated in GEE by: Samapriya Roy

Keywords: crop suitability, climate change, agriculture, Africa, food security, land use planning, climate variability, fuzzy logic, crop modeling, optimal sowing dates, multiple cropping, climate adaptation

Last updated: 2025-10-01

Projects Csb (`docs/projects/csb.md`)

USDA Crop Sequence Boundaries 2018-2025

The Crop Sequence Boundaries (CSB), developed in collaboration with the USDA's Economic Research Service, provides estimates of field boundaries, crop acreage, and crop rotations across the contiguous United States. This dataset utilizes satellite imagery along with other public data and is open source, enabling users to conduct area and statistical analysis of planted U.S. commodities. It offers valuable insights into farmer cropping decisions and practices.

NASS required a representative field dataset for predicting crop planting based on common rotations like corn-soy, while the Economic Research Service (ERS) employs the CSB to study changes in farm management practices such as tillage and cover cropping over time. The CSB dataset represents non-confidential single crop field boundaries within a specified timeframe. It does not include personal identifying information, ownership boundaries, or tax parcels. The data is sourced from satellite imagery and publicly available information, excluding contributions from producers or agencies like the Farm Service Agency. For access and further information, you can visit the CSB website. Explore the CSB GitHub repository for the codebase, and review the metadata associated with the dataset.

Crop Sequence Boundaries (CSB) represent geospatial algorithm-generated field polygons, originating from the NASS Cropland Data Layer (CDL). These polygonal entities cater to the demands of applications reliant on gridded datasets, necessitating analytical units for streamlined data aggregation. The primary objective of CSBs is to furnish comprehensive coverage spanning the contiguous 48 United States, ensuring precision and replicability across multiple years. These structures are forged by amalgamating historical CDLs within a specified time frame, while also integrating road and rail networks to accurately depict crop sequences within these simulated fields. The most recent eight-year window covers the 2018 to 2025 growing seasons, and the full archive of eight-year windows runs from 2008-2015 onward.

Citations

text
Hunt, Kevin A., Jonathon Abernethy, Peter Beeson, Maria Bowman, Steven Wallander, and Ryan Williams. "Crop Sequence Boundaries (CSB): Delineated
Fields Using Remotely Sensed Crop Rotations."

Abernethy, Jonathon, Peter Beeson, Claire Boryan, Kevin Hunt, and Luca Sartore. "Preseason crop type prediction using crop sequence boundaries." Computers and Electronics in Agriculture 208 (2023): 107768.

Dataset structure and preprocessing

The datasets are made available as feature collections in Earth Engine for each state, where the 1825 in the collection name represents the 2018-2025 growing seasons. The state names are part of the feature collection name. While it may not be necessary it is possible to merge them into a single collection for those who would want to run some analysis on a combined feature collection.

Note that the state collections for this window carry crop history attributes for CDL years 2018 through 2025 (for example CROP18 through CROP25), so scripts written against the 2016-2023 assets need their attribute names updated as well as their paths.

Earth Engine Snippet: Source

text
/* Detailed source-code truncated for AI context efficiency. */

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/USDA-CSB-APP

App code: You can find the app here

License and Liability

The USDA NASS Crop Sequence Boundaries and the data offered at https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries are provided to the public as is and are considered public domain and free to redistribute. Users of the Crop Sequence Boundaries (CSB) are solely responsible for interpretations made from these products. The CSB are provided 'as is' and the USDA NASS does not warrant results you may obtain using the data. Contact our staff at ([email protected]) if technical questions arise.

Created by: USDA NASS, USDA ERS

Curated in GEE by : USDA NASS, USDA ERS, Samapriya Roy

keywords: agricultural lands, USDA, crop layer, CDL, crop sequence boundary

Last updated in GEE: 2026-03-27

Changelog notes from Source

  • The 2018-2025 CSB was released March 27, 2026, and is the window shown in the NASS interactive map. The current algorithm version is v2024.
  • The 2017-2024 CSB was released in 2025, extending the archive by one eight-year window.
  • The 2016-2023 CSB was released April 5, 2024. Concurrent with that release, all historic CSB data was re-released to reflect updates in methodology; that version is revision 2.3 (rev23), and the rev23 label still applies to the downloadable national archives.
  • The current archive of these datasets spans 8-year time frames for all years from 2008 to current (2008-2015 through 2018-2025). Custom time frames can be created using the Github code.

Changelog for this page

  • 2026-08-30 β€” Updated to the 2018-2025 window: state Earth Engine assets repointed to projects/nass-csb/assets/CSB1825_rev23/CSBXX1825, metadata link updated to the 2025 metadata page, release date added, and the retired 2016-2023 (csb1623) asset list and combined CSB_1623 collection removed since those assets no longer resolve.
  • 2024-05-25 β€” Original page documenting the 2016-2023 (csb1623) state assets and the combined CSB_1623 collection.

Projects Csi (`docs/projects/csi.md`)

Carbon Security Index

This dataset presents an index developed to assess carbon security across the sagebrush steppe in the Great Basin. The Carbon Security Index (CSI) incorporates data from three key sources: fractional plant cover from the Rangeland Analysis Platform, a fire probability model specific to the Great Basin (Smith et al., 2023), and a resistance and resilience dataset for the sagebrush steppe (Chambers et al., 2014, 2017).

The CSI is calculated as follows:

CSI = Preferred Rangeland Cover Index + Resistance & Resilience – P(Fire)

The resulting index ranges from -2 to +2 and allows for spatial comparisons of carbon security across the region.

???+ note

text
**The associated paper for this dataset has been accepted as part of a special issue but does not yet have a DOI assigned. A preprint version was not made available. Please note that the listed citations do not refer to this paper.**

Supplemental Citation

text
[Smith, J.T., Allred, B.W., Boyd, C.S., Davies, K.W., Jones, M.O., Kleinhesselink, A.R., Maestas, J.D., Naugle, D.E. (2023). Where There's Smoke, There's Fuel: Dynamic Vegetation Data Improve Predictions of Wildfire Hazard in the Great Basin. Rangeland Ecol. Manage. 89:20-32. https://doi.org/10.1016/j.rama.2022.07.005](https://doi.org/10.1016/j.rama.2022.07.005)

[Chambers, J.C., Miller, R.F., Board, D.I., Pyke, D.A., Roundy, B.A., Grace, J.B., Schupp, E.W., Tausch, R.J. (2014). Resilience and Resistance of Sagebrush Ecosystems: Implications for State and Transition Models and Management Treatments. Rangeland Ecol. Manage. 67:440-454. https://doi.org/10.2111/REM-D-13-00074.1](https://doi.org/10.2111/REM-D-13-00074.1)

[Chambers, J.C., Maestas, J.D., Pyke, D.A., Boyd, C.S., Pellant, M., Wuenschel, A. (2017). Using Resilience and Resistance Concepts to Manage Persistent Threats to Sagebrush Ecosystems and Greater Sage-grouse. Rangeland Ecol. Manage. 70:149-164. https://doi.org/10.1016/j.rama.2016.08.005](https://doi.org/10.1016/j.rama.2016.08.005)

Earth Engine Snippet

js
var csi_great_basin = ee.ImageCollection("projects/sat-io/open-datasets/CSI/Great_Basin");

var mosaic = csi_great_basin.mosaic();

var palette = [
  'FF0000',
  'FFA500',
  'FFFF00',
  'ADFF2F',
  '008000'
];

var visParams = {
  min: -1,
  max: 1,
  palette: palette
};

Map.centerObject(csi_great_basin.first(),6)

Map.addLayer(mosaic.select('CSI'), visParams, 'Great Basin Carbon Security Index');

var snazzy = require("users/aazuspan/snazzy:styles");
snazzy.addStyle("https://snazzymaps.com/style/15/subtle-grayscale", "Greyscale");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CARBON-SECURITY-INDEX

License

This dataset is made available under Creative Commons Attribution 4.0 International license.

Keywords: rangelands, sagebrush steppe, Great Basin, ecosystem function, environmental assessment and monitoring, environmental management

Curated in GEE by: Samapriya Roy

Last updated : 2024-07-24

Projects Ctrees Amazon (`docs/projects/ctrees_amazon.md`)

High Resolution Amazon Canopy Tree Height Dataset

This dataset provides high-resolution (~4.78 m) tree canopy height for the Amazon forest for the period 2020-2024. It was generated using a U-Net deep learning model adapted for regression, trained with Planet NICFI satellite images and informed by LiDAR-derived canopy height models (CHMs). The model predicts mean tree canopy height and has demonstrated a mean error of 3.68 m in validation samples, successfully estimating canopy heights up to 40-50 m with relatively low systematic bias. The study determined that the Amazon forest has an average canopy height of approximately 22 m. You can find additional information in the preprint

Key Features and Details

Spatial Resolution: ~4.78 meters

Temporal Coverage: Composite for 2020-2024. The model was trained using Planet NICFI images from 2015-12-01 to 2024-06-01 and LiDAR data collected between 2008 and 2018. The final Amazon forest map is a mean for the period 2020-2024.

Coverage: Amazon forest domain (approximately 8,260,920 kmΒ²)

Accuracy: Mean Absolute Error (MAE) of 3.68 m on validation samples. The model estimates heights up to 40-50 m without significant saturation.

Data Format: To obtain the actual height in meters, the pixel values in the asset must be divided by 2.5. The original CHM was multiplied by 2.5 and saved as integer 8 bits.

Mean Amazon Canopy Height: Estimated at ~22.09 m

Data Sources

Satellite Imagery: Planet NICFI (Norway's International Climate and Forest Initiative) images (Red, Green, Blue, Near-Infrared bands) at ~4.78 m spatial resolution.

Reference Data (LiDAR):

  • Brazilian Agricultural Research Corporation (Embrapa) - "Sustainable Landscapes Brazil" Program (2015-2018)
  • National Institute for Space Research (INPE, Brazil) - EBA (Biomass Estimation of the Amazon) project (2016-2018)
  • SΓ£o Paulo City Hall (Brazil) - LiDAR dataset covering the SΓ£o Paulo Metropolitan Region (2017)

Building Footprints (for masking): Google Open Buildings dataset for South America

Citation

text
Wagner, F.H., Dalagnol, R., Carter, G., Hirye, M.C.M., Gill, S., Takougoum, L.B.S., Favrichon, S., Keller, M., Ometto, J.P.H.B., Alves, L., Creze, C., George-Chacon, S.P., Li, S., Liu, Z., Mullissa, A., Yang, Y., Santos, E.G., Worden, S.R., Brandt, M., Ciais, P., Hagen, S.C. and Saatchi, S. (2025)
Wall-to-wall Amazon forest height mapping with planet NICFI, Aerial LiDAR, and a U-Net regression model. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.70041

Dataset Preprocessing for Earth Engine

The datasets are distributed over 22,063 tiles which were downloaded and a mosaiced product was created for ingest. The scale can be applied in code and was added as a property to the image along with the units. As a result of the mosaic operation the tile naming convention with traceability back to the image was removed as a result of which the grid was added as a separate asset for allow traceability back to individual tiles.

Earth Engine Snippet

js
var amazonCanopyHeight = ee.Image("projects/sat-io/open-datasets/CTREES/AMAZON-CANOPY-TREE-HT");
var amazonCanopyHeight_tiles = ee.FeatureCollection("projects/sat-io/open-datasets/CTREES/CTREES-AMAZON-TILE-LOCATOR")

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CTREES-AMAZON-CANOPY-TREE-HT

License

The underlying Planet NICFI satellite imagery is available for non-commercial purposes under specific agreements with Planet and Norway's International Climate and Forest Initiative. The LiDAR datasets used for training have their own respective licenses, generally public or available for research.

Provided by: Wagner et al 2025, ctrees.org

Curated in GEE by: Samapriya Roy

Keywords : Canopy Height, Amazon Forest, Tree Height, Remote Sensing, Deep Learning, U-Net, Planet NICFI, LiDAR, Tropical Forests, Forest Structure

Last updated in GEE: 2025-11-18

Changelog

  • Updated Citation for Publication

Projects Ctrees Global (`docs/projects/ctrees_global.md`)

CTrees Global Aboveground Biomass 100m (2000–2025)

!!! note "Preprint Notice"
This dataset is associated with a non-peer-reviewed preprint submitted to EarthArXiv (May 7, 2026). The methodology and results have not yet undergone formal peer review. Users are encouraged to review the preprint directly and exercise appropriate caution when citing or applying this dataset in research. This page will be updated once this has been published

Accurate, high-resolution estimation of forest aboveground biomass (AGB) is critical for quantifying terrestrial carbon stocks and informing climate mitigation policy, yet existing global products remain constrained by coarse spatial resolution and limited temporal coverage. This dataset from CTrees presents a wall-to-wall global AGB product at 100 m native spatial resolution spanning 2000–2025, trained with a DenseNet deep learning architecture using independent AGB reference labels from ALS-derived AGB, field-plot and forest-inventory samples from the research community, selected regional and national inventories, and mangrove AGB training samples from global coastal regions. The predictor stack integrates Landsat surface reflectance time series, ALOS PALSAR-1/2 L-band backscatter, GEDI and ICESat-2 canopy-height metrics (used only as ancillary structural layers), and topographic data. On a held-out validation set of approximately 1.01 million pixels compiled across eco-regions, the product achieves RΒ² = 0.741, RMSE = 59.5 Mg ha⁻¹, and bias = βˆ’4.82 Mg ha⁻¹. Further details about the dataset and underlying methodology can be found in the CTrees AGB Carbon Data Brief.

Band Description

Each annual image in the collection contains two bands. Raw pixel values are stored as signed int16 with a scale factor of 0.1; divide raw values by 10 to obtain values in Mg ha⁻¹.

Band Units Scale Factor Description
agb Mg/ha 0.1 Aboveground biomass of dry matter in live woody vegetation. Generated by a cluster-aware DenseNet framework integrating Landsat optical imagery, ALOS PALSAR L-band microwave backscatter, and lidar/field reference data to translate structural height metrics into biomass density.
uncertainty Mg/ha 0.1 Pixel-level residual uncertainty in AGB estimates. Uncertainty grows with biomass magnitude, consistent with optical and SAR saturation in dense-canopy conditions.

!!! warning "Scale Factor"
Raw pixel values must be divided by 10 to obtain true AGB values in Mg/ha (e.g., a raw value of 352 = 35.2 Mg/ha).
For conversion to carbon (C), multiply AGB by 0.5.
For conversion to COβ‚‚ equivalent, multiply carbon by 3.667.

Image Properties

Each image carries the following metadata properties:

Property Value Description
agb_scale_factor 0.1 Multiply raw AGB band values by this factor
agb_units Mg/ha Units after applying scale factor
uncertainty_scale_factor 0.1 Multiply raw uncertainty band values by this factor
uncertainty_units Mg/ha Units after applying scale factor
carbon_conversion_factor 0.5 Multiply AGB by this to get carbon mass
co2e_conversion_factor 3.667 Multiply carbon by this to get COβ‚‚ equivalent
spatial_resolution_m 100 Native spatial resolution in meters
year e.g. 2000 Year of the annual AGB estimate

Citation

text
Yang, Y., Saatchi, S., Kwon, N., Lin, W.T., Liu, Z., Li, S., DalAgnol, R., Sagang, L.B., 2026.
From Snapshot Maps to Continuous Monitoring of Global Forest Carbon at 100 m Resolution
(2000–2025). EarthArXiv (preprint). https://doi.org/10.31223/X5KJ4Q

Dataset Citation

text
CTrees. (2026). CTrees Global Aboveground Biomass 100m (2000–2025) [Data set].
https://doi.org/10.82924/7vmb-zv66

Earth Engine Snippet

js
var collection = ee.ImageCollection("projects/sat-io/open-datasets/CTREES-GLOBAL-AGB-100M");
print(collection);
print(collection.size());

// --- Rescale function: apply scale factor and mask pixels <= 0 ---
function rescaleAGB(image) {
  var scaled = image.multiply(image.getNumber('agb_scale_factor'));
  return scaled.updateMask(scaled.gt(0)).copyProperties(image, image.propertyNames());
}

// --- Temporal window ---
var startDate = '2000-01-01';
var endDate   = '2025-12-31';

var filtered = collection
  .filterDate(startDate, endDate)
  .map(rescaleAGB);

var agb = filtered.sort('system:time_start', false);

// --- Visualization parameters ---
var visParams = {
  bands: ['agb'],
  min: 0,
  max: 400,
  palette: [
    '#f7fcf5', '#d9f0d3', '#b2e0a8', '#84ca7e', '#5db96a',
    '#38a152', '#1f7f3b', '#0d5c26', '#00441b', '#023640',
    '#042864', '#08306b'
  ]
};

Map.setCenter(-62, -4, 4);
Map.addLayer(agb, visParams, 'AGB (Mg/ha)');

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CTREES-GLOBAL-AGB100M

License & Usage

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0) license. You are free to copy, redistribute, and build upon the material for any purpose, including commercially, provided you give appropriate credit, provide a link to the license, and indicate if changes were made.

Provided by: Yang et al 2026

Curated in GEE by: Samapriya Roy

Keywords: Aboveground Biomass, AGB, Forest Carbon, Global, 100m, Deep Learning, DenseNet, Landsat, ALOS PALSAR, GEDI, ICESat-2, Temporal Change, REDD+, Carbon Monitoring, CTrees

Last updated: 2026-06-02

Projects Cygnss (`docs/projects/cygnss.md`)

CYGNSS Fractional Inundation

Data span August 2018 to December 2025 at 3 km resolution, with daily files representing either daily or 3-daily aggregated interpolated data. Coverage is limited to Β±38 degrees latitude by the CYGNSS orbital inclination.

The UCAR/CU CYGNSS inundation maps provide fractional surface water extent derived from NASA's Cyclone Global Navigation Satellite System (CYGNSS), a constellation of eight microsatellites launched in December 2016. Each satellite carries a GNSS-Reflectometry (GNSS-R) receiver that records L-band GPS signals reflected from the Earth's surface. Because the transmitters are already on orbit, GNSS-R receivers are inexpensive to build and fly, which makes constellations β€” and therefore short revisit times β€” practical in a way that single-satellite radars are not. Surface reflectivity (Ξ“) at L-band is sensitive to the dielectric constant and micro-scale roughness of the reflecting surface, both of which change sharply when a landscape floods.

Inundation fraction is retrieved by treating the observed reflectivity of a 3 km grid cell as the weighted average of the reflectivity of its inundated and un-inundated fractions, with the weights equal to the respective cell fractions. Inverting this relationship requires ancillary estimates of soil moisture (SMAP L3 Enhanced), soil surface roughness, water surface roughness, and an empirical correction for the attenuating effect of above-ground biomass. Soil and water roughness are parameterized as spatially varying but temporally static fields calibrated against 2018 observations; soil moisture varies through time.

Because the eight satellites sample the surface pseudo-randomly and cannot completely cover it within a few days, observations are aggregated over 3-day windows and spatially interpolated using the Previously-Observed Behavior Interpolation (POBI) method, which derives historical relationships between reflectivity in neighboring grid cells and applies them to unobserved cells. POBI is an exact interpolator β€” observed values are unchanged β€” so users who prefer to avoid interpolated data can restrict their analysis to observed cells using the interpolation flag band.

Data span August 2018 to December 2025 present at 3 km resolution, with daily files representing the mean inundation fraction for the current day and previous two days. Coverage is limited to Β±38 degrees latitude by the CYGNSS orbital inclination.

Available Datasets

Dataset Type Description Status Earth Engine Collection
CYGNSS Inundation Daily 5-band fractional inundation, anomalies, and interpolation flag (2018-2025) Ongoing projects/sat-io/open-datasets/CYGNSS/INUNDATION

Dataset Details

??? example "The CYGNSS inundation dataset details can be found here"

text
| Characteristic | Description |
  | --- | --- |
  | Name | UCAR/CU CYGNSS Inundation Maps |
  | Provider | University Corporation for Atmospheric Research (UCAR) and University of Colorado Boulder |
  | Funding | NASA Terrestrial Hydrology Program (awards 80NSSC19K0046, 80NSSC18K1430) |
  | Data Type | Raster (5-band, UInt8) |
  | Coverage | Global, Β±38 degrees latitude (CYGNSS orbital inclination) |
  | Temporal Range | August 2018 - December 2025 |
  | Temporal Resolution | Daily files; 3-day aggregation window |
  | Spatial Resolution | 3 km x 3 km |
  | Coordinate System | WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933) |
  | Version | 3.2 |
  | Fill Value | 255 |

Band Descriptions

??? example "The CYGNSS inundation dataset includes the following bands"

text
| Band | Description | Units |
  | --- | --- | --- |
  | inundation_1day | 1-day CYGNSS inundation fraction, observed cells only | %, divide by 100 for fraction |
  | inundation_3day | 3-day CYGNSS average inundation fraction, interpolated onto a regular grid | %, divide by 100 for fraction |
  | interpolation_flag | Whether a grid cell is interpolated (1) or observed (0) | None |
  | inundation_anomaly_1day | 1-day inundation anomaly, observed cells only | %, subtract 100 then divide by 100 |
  | inundation_anomaly_3day | 3-day average inundation anomaly, interpolated onto a regular grid | %, subtract 100 then divide by 100 |

  All bands use 255 as the fill value. Anomaly bands carry a +100 bias so that negative anomalies can be stored in an unsigned type β€” subtract 100 before converting to fractional inundation. Anomalies are computed relative to a long-term baseline climatology.

Image Properties

??? example "Each image carries the following properties"

text
| Property | Description |
  | --- | --- |
  | year | Calendar year of the observation |
  | doy | Day of year (1-366) |
  | date | Observation date as YYYY-MM-DD |
  | month | Calendar month |
  | version | Product version (3.2) |
  | scale_factor | 0.01 β€” multiply band values to obtain fraction |
  | anomaly_offset | 100 β€” subtract from anomaly bands before scaling |
  | fill_value | 255 |
  | system:time_start | UTC midnight of the observation date |
  | system:time_end | UTC midnight of the following day (exclusive) |

Uncertainty and Known Limitations

??? example "Users should be aware of the following sources of uncertainty"

text
| Source | Effect |
  | --- | --- |
  | Reflectivity calibration | Uncertainty in Ξ“ is roughly Β±1.74 dB, much of it attributable to GPS satellites switching to 'flex power mode' in February 2020. CYGNSS data are not calibrated for land remote sensing, which can bias entire tracks up or down. |
  | Water roughness | The largest known unknown. Water roughness is held temporally static, but varies with wind. Where surface water is rougher than the parameterized value, inundation is under-retrieved. |
  | Soil moisture | SMAP retrievals are themselves biased high by surface water, and high soil moisture values push the retrieval toward underestimating inundation during floods. |
  | High inundation fractions | Reflectivity becomes progressively less sensitive to inundation as fraction rises above ~0.5, so small errors in Ξ“ can translate into inundation differences greater than 0.3. |
  | Dense vegetation | Retrievals underestimate inundation in flooded forest; the biomass correction does not account for seasonal canopy water content or structure. |
  | Complex terrain | Isolated cells with anomalously high inundation appear in mountainous regions, resembling speckle noise. |
  | Spatial interpolation | POBI performs best where inundation follows historical patterns and less well during floods with return intervals longer than the 2017-2018 training period. |

  Compared against PALSAR-2 over the Amazon, retrievals agree closely (RMSD 0.07, rΒ² of 0.91) but underestimate at high water fractions. Against the Dartmouth Flood Observatory for Cyclone Idai, mean inundation was 0.06 low with an RMSD of 0.16. Incorporating several inundation sources is likely to be more informative than relying on any one alone.

Data Access

The source netCDF files are hosted on the COSMIC data server. The Earth Engine collection hosts the same retrievals converted to Cloud-Optimized GeoTIFF and updated to present day.

Citation

text
Chew, C., Small, E., Huelsing, H. 2023. Flooding and inundation maps using interpolated CYGNSS reflectivity observations. Remote Sensing of Environment 293: 113598. https://doi.org/10.1016/j.rse.2023.113598

Related publications:

  • Chew, C.; Small, E. 2020. Estimating inundation extent using CYGNSS data: A conceptual modeling study. Remote Sensing of Environment 246: 111869. https://doi.org/10.1016/j.rse.2020.111869
  • Chew, C. 2021. Spatial interpolation based on previously-observed behavior: a framework for interpolating spaceborne GNSS-R data from CYGNSS. Journal of Spatial Science. https://doi.org/10.1080/14498596.2021.1942253
  • Chew, C.; Small, E. 2020. Description of the UCAR/CU Soil Moisture Product. Remote Sensing 12(10): 1558. https://doi.org/10.3390/rs12101558
  • Chew, C.; Reager, J.T.; Small, E. 2018. CYGNSS data map flood inundation during the 2017 Atlantic hurricane season. Scientific Reports 8. https://doi.org/10.1038/s41598-018-27673-x
  • Chew, C.; Small, E. 2018. Soil moisture sensing using spaceborne GNSS reflections: comparison of CYGNSS reflectivity to SMAP soil moisture. Geophysical Research Letters 45: 4049-4057. https://doi.org/10.1029/2018GL077905
  • Baldysz, Z.; Baranowski, D. B.; Flatau, P. J.; Flatau, M. K.; Chew, C. 2025. Detecting localized floods in tropical regions with CYGNSS SmallSat constellation: A proof of concept from the Maritime Continent. Geophysical Research Letters 52: e2025GL118111. https://doi.org/10.1029/2025GL118111

Earth Engine Snippet

javascript
var cygnss = ee.ImageCollection("projects/sat-io/open-datasets/CYGNSS/inundation");

// Scale to fractional inundation and mask fill values
var toFraction = function(image) {
  return image.select(['inundation_1day', 'inundation_3day'])
              .updateMask(image.select('inundation_3day').neq(255))
              .divide(100)
              .copyProperties(image, ['system:time_start', 'doy', 'year']);
};

// Anomalies carry a +100 bias
var toAnomaly = function(image) {
  return image.select('inundation_anomaly_3day')
              .updateMask(image.select('inundation_anomaly_3day').neq(255))
              .subtract(100).divide(100)
              .copyProperties(image, ['system:time_start']);
};

// Restrict to directly observed cells only
var observedOnly = function(image) {
  return image.updateMask(image.select('interpolation_flag').eq(0));
};

var palette = ['f7f4e9', 'c8c88c', '7ab88a', '2b8cbe', '08306b'];
Map.addLayer(cygnss.filterDate('2024-08-01', '2024-09-01').map(toFraction).mean(),
             {min: 0, max: 1, bands: ['inundation_3day'], palette: palette},
             'Mean fractional inundation, Aug 2024');

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/CYGNSS-INUNDATION

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

Created by: Clara Chew, Zofia Baldysz, Eric Small, and Hannah Huelsing β€” University Corporation for Atmospheric Research (UCAR) and University of Colorado Boulder, with funding from the NASA Terrestrial Hydrology Program

Curated in GEE by: Samapriya Roy

Keywords: inundation, flooding, surface water, CYGNSS, GNSS-R, reflectometry, wetlands, hydrology

Last updated: 2026-08-05

Projects Daily Lst (`docs/projects/daily_lst.md`)

MODIS Gap filled Long-term Land Surface Temperature Daily (2003-2020)

High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. In this study the authors developed a novel spatiotemporal gap-filling framework by implementing data preprocessing (filtering pixels with low data quality and gap-filling missing values at one overpass using values at one of the other three overpasses each day) and spatiotemporal fitting (filtering the long-term trend (overall mean) of observations in each pixel, and then spatiotemporally interpolating residuals between observations and overall mean values for each day, and finally adding the overall mean and interpolated residuals), to generate a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. The paper on the gap-filling method will be published in near future.

The method was implemented to create a global gap filled LST observation.. The cross-validation indicates that the average root mean squared error (RMSE) for mid-daytime (1:30pm) and mid-nighttime (1:30am) LST is 1.88K and 1.33K, respectively. The gap-filled LST in the unit of 0.1 Celsius temperature (0.1 degree C) .You can read the abstract here

The datasets and entire collection is available at Figshare.

Citation

Paper Citation

text
Zhang, T., Zhou, Y., Zhu, Z., Li, X., and Asrar, G. R.: A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)
Earth Syst. Sci. Data, 14, 651–664, https://doi.org/10.5194/essd-14-651-2022, 2022

Collection Citation

text
Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem (2021): A global seamless 1 km resolution daily
land surface temperature dataset (2003 – 2020). Iowa State University. Collection. https://doi.org/10.25380/iastate.c.5078492.v1

Earth Engine Snippet Climate variables

js
var gf_day_1km = ee.ImageCollection("projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km");
var gf_night_1km = ee.ImageCollection("projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/MODIS-GAPFILLED-LST-DAILY

License

These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.

Created by: Zhang, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem

Curated in GEE by: Samapriya Roy

Keywords: Land Surface Temperature, LST, MODIS, gapfilled

Last updated: 2021-10-03

Projects Daylight Maps (`docs/projects/daylight_maps.md`)

Daylight Map Distribution map data

Daylight is a complete distribution of global, open map data that’s freely available with support from community and professional mapmakers. We combine the work of global contributors to projects like OpenStreetMap with quality and consistency checks from Daylight mapping partners to create a free, stable, and easy-to-use street-scale global map. Daylight Map Distribution will include a new dataset consisting of vectorized landcover features derived from the European Space Agency’s 2020 World Cover (10m) rasters. This dataset provides global coverage and is suitable for use in maps up to 1:1 million (zoom level 8).

Dataset structure

Dataset releases are created by the team periodically and will be ingested accordingly into the GEE collection.

Attribution

text
* Β© OpenStreetMap contributors available under the Open Database License (www.openstreetmap.org/copyright)
* Building data Β© OpenStreetMap contributors, Microsoft, Esri Community Maps contributors
* Australia Building Footprints (github.com/microsoft/AustraliaBuildingFootprints)
* Canadian Building Footprints (github.com/microsoft/CanadianBuildingFootprints)
* Uganda/Tanzania Building Footprints (github.com/microsoft/Uganda-Tanzania-Building-Footprints)
* US Building Footprints (github.com/microsoft/USBuildingFootprints)

Earth Engine snippet

js
var water_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/DAYLIGHTMAP/water_polygons");
var land_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/DAYLIGHTMAP/land_polygons");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/DAYLIGHT-LAND-WATER-POLY

Landcover layer: ESA 2020

js
var landcover = ee.FeatureCollection("projects/sat-io/open-datasets/DAYLIGHTMAP/LANDCOVER_ESA_2020");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/DAYLIGHT-LANDCOVER

License

Daylight Map Distribution is open data, licensed under the Open Data Commons Open Database License (ODbL). Daylight is built from upstream sources, primarily from OpenStreetMap contributors with optional additions from Esri Community Maps Contributors and Microsoft Corporation.

Provided by: Daylight Map Distribution

Curated in GEE by: Samapriya Roy

Keywords: Daylight Map Distribution, landcover, land polygons, water polygons, OSM, OpenStreetMap

Last updated in GEE: 2023-10-20

Projects Dea Croplands (`docs/projects/dea_croplands.md`)

Digital Earth Africa's cropland extent map Africa 2019

These maps shows the estimated location of croplands in the following countries for the period January to December 2019 where cropland is defined as a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date. It was also noted that "This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation." The provisional cropland extent maps have a resolution of 10 metres, and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built seperately using extensive training data from Eastern, Western, Northern, and Sahel Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask. Easiest place to download the datasets is from AWS Open data registry

  • crop_mask: Entire African continent
  • crop_mask_eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda, and Burundi
  • crop_mask_western: Nigeria, Benin, Togo, Ghana, Cote d’Ivoire, Liberia, Sierra Leone, Guinea, and Guinea-Bissau
  • crop_mask_northern: Morocco, Algeria, Tunisia, Libya, and Egypt
  • crop_mask_sahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia, and Djibouti
  • crop_mask_southern: South Africa, Namibia, Botswana, Lesotho, and Eswanti
  • crop_mask_southeast: Zimbabwe, Zambia, Mozambique, and Malawi
  • crop_mask_central: Angola, Democratic Republic of the Congo, Congo, Gabon, Cameroon, Equatorial Guinea, and Central African Republic
  • crop_mask_indian_ocean: Madagascar, Mauritius, Reunion, and Comoros

The products contain three measurements:

  • mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.

  • prob: This band displays the prediction probabilities for the β€˜crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted β€˜crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.

  • filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm see this paper for details on the algorithm used. During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the β€˜salt and pepper’ effect typical of classifying pixels is diminished.

Band ID Description Value range Data type NoData/Fill value
mask crop extent (pixel) 0Β -Β 1 uint8 0
prob crop probability (pixel) 0Β -Β 100 uint8 0
filtered crop extent (object-based) 0Β -Β 1 uint8 0

You can details on the method and more here

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Preprocessing for GEE

All images were download and merged into single collections. The metadata tags including regions, versions were maintained from the STAC metadata JSON files provided for the regional downloads. Validation datasets for each region were downloaded ingested and merged into a single feature collection.

Earth Engine snippet

js
var filtered = ee.ImageCollection("projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/filtered")
var mask = ee.ImageCollection("projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/mask");
var prob = ee.ImageCollection("projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/prob");
var validation = ee.FeatureCollection("projects/sat-io/open-datasets/DEAF/CROPLAND-EXTENT/validation");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/DEA-CROPLAND-EXTENT

License

This dataset is made available under the CC BY Attribution 4.0 International License.

Created by: Digital Earth Africa

Curated by: Samapriya Roy

Keywords: agriculture, cog, deafrica, earth observation, food security, geospatial, satellite imagery, stac,sustainability

Last updated in GEE: 2023-03-13

Projects Dea Lc (`docs/projects/dea_lc.md`)

Digital Earth Australia(DEA) Landsat Land Cover 25m v1.0.0

Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures.β€―A consistent, Australia-wide land cover product helpsβ€―understanding ofβ€―how the different parts of the environment change andβ€―inter-relate.β€―Earth observation data recorded overβ€―a period of timeβ€―firstly allows the observation ofβ€―the state of land cover at a specific time andβ€―secondly the way that land cover changes by comparison between times.

DEA Land Cover provides annual land cover classifications for Australia using the Food and Agricultureβ€―Organisationβ€―Land Cover Classification System taxonomy Version 2 (Di Gregorio and Jansen, 1998; 2005). DEA Land Coverβ€―classifications have been generated by combining quantitative (continuous) or qualitative (thematic) environmental information (referred to as Essential Descriptors; EDs) derived from Landsatβ€―satelliteβ€―sensor data. Several EDs have been generated previously by Geoscience Australia, including annual water summaries (Mueller et al., 2016), vegetation fractional cover (Scarth et al., 2010), mangrove extent (Lymburner et al., 2020) and the Inter Tidal Extent Model (ITEM; Sagar et al., 2017), whilst others have been developed more recently. These EDs have been combined to generate detailed, consistent and expandable annual classifications of Australia’s land cover from 1988 through to 2020.β€―

Additional information including descriptors above can be found here and you can also explore the map here

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Data preprocessing

Datasets were downloaded from Amazon S3 buckets and each years tiles were composited to create single year derivatives. Start and end date for years were also added to metadata and the collection included the version 1.0.0 as v100. Since this is a classified imagery MODE pyramiding scheme was used and default nodata value from tif at 0 was utilized for no data value during upload.

Citation

text
Lucas R, Mueller N, Siggins A, Owers C, Clewley D, Bunting P, Kooymans C, Tissott B, Lewis B, Lymburner L, Metternicht G. Land Cover Mapping using Digital Earth Australia. Data.
2019; 4(4):143. https://doi.org/10.3390/data4040143

Christopher J. Owers, Richard M. Lucas, Daniel Clewley, Carole Planque, Suvarna Punalekar, Belle Tissott, Sean M. T. Chua, Pete Bunting, Norman Mueller & Graciela Metternicht
(2021) Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development, Big Earth Data, 5:3, 368-
390, DOI: 10.1080/20964471.2021.1948179

Earth Engine Snippet

js
var dea_lcv100 = ee.ImageCollection("projects/sat-io/open-datasets/DEA/landcover_v100")

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/DEA-LANDSAT-LC

License

Creative Commons Attribution-Share Alike 4.0 International License

Created by: Geoscience Australia and Digital Earth Australia

Curated by: Samapriya Roy

Keywords: : Australia, land cover, remote sensing, landsat, satellite

Last updated in GEE: 2022-03-05

Projects Dea Shorlines (`docs/projects/dea_shorlines.md`)

Digital Earth Australia Coastlines

Digital Earth Australia Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire Australian coastline from 1988 to the present. The product combines satellite data from Geoscience Australia's Digital Earth Australia program with tidal modelling to map the most representative location of the shoreline at mean sea level for each year. The product enables trends of coastal retreat and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.

The ability to map shoreline positions for each year provides valuable insights into whether changes to our coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting our coastlines and potentially assist planning and forecasting for future scenarios. You can find additional details here and you can download the datasets here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Annual shoreline vectors that represent the median or β€˜most representative’ position of the shoreline at approximately 0 m Above Mean Sea Level for each year since 1988. Dashed shorelines have low certainty and Annual shorelines include the following attribute fields:

Attribute Description
year The year of each annual shoreline.
certainty A column providing important data quality flags for each annual shoreline.
tide_datum The tide datum of each annual shoreline (e.g. β€œ0 m AMSL”).
id_primary The name of the annual shoreline’s Primary sediment compartment from the Australian Coastal Sediment Compartments framework.

Citation

text
Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia
Coastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268

Publications

text
Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic
coastline at mean sea level using three decades of Landsat imagery. Remote Sensing of
Environment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734

Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into
Australia's coastal change using a national dataset derived from the multi-decadal Landsat
archive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/
j.ecss.2021.107712

Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019). Sub-pixel
waterline extraction: Characterising accuracy and sensitivity to indices and spectra. Remote
Sensing, 11(24), 2984. Available: https://www.mdpi.com/2072-4292/11/24/2984

Earth Engine Snippet

js
var shoreline_annual = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_3-0-0_shorelines_annual");
var hotspot_zoom_1 = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_3-0-0_hotspots_zoom_1");
var hotspot_zoom_2 = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_3-0-0_hotspots_zoom_2");
var hotspot_zoom_3 = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_3-0-0_hotspots_zoom_3");
var rate_of_change = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_3-0-0_rates_of_change");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/DEA-Shorelines

License

These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.

Created by: Digital Earth Australia

Curated in GEE by : Samapriya Roy

Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction,
subpixel waterlines, coastal change, DEA CoastLines, coastline data, coastal erosion

Changelog

  • DEA Coastlines product was updated to version 3.0.0
  • In August 2024 , the DEA Coastlines product was updated to version 2.2.0. This update consists of the addition of annual shoreline data for 2023.
  • In August 2023, the DEA Coastlines product was updated to version 2.1.0. This update consists of the addition of annual shoreline data for 2022. The 2022 shoreline is interim data that is subject to change, and will be updated to a final version in the following 2023 DEA Coastlines update (in July 2024).

Last updated : 2026-01-27

Projects Deaf Shorlines (`docs/projects/deaf_shorlines.md`)

Digital Earth Africa Coastlines

The Digital Earth Africa Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline. This is a provisional service that has been generated for 2000 to 2021 and we would like to improve and operationalize with users.

The product combines satellite data from the Digital Earth Africa program with tidal modelling to map the typical location of the coastline at mean sea level each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.

The ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers, and policy makers to assess impact from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios. You can find additional details here and you can download the datasets here

Acknowledgment

The Coastlines algorithms incorporated in this product are the work of Robbi-Bishop Taylor, Rachel Nanson, Stephen Sagar, and Leo Lymburner, Geoscience Australia. Digital Earth Africa acknowledges the work done by the Centre de Suivi Ecologique (CSE), Dakar, in assessing the accuracy of the service with the participation of West African WACA stakeholders. Acknowledgements also go to the Regional Center for Mapping Resources for Development (RCMRD) for stakeholder engagement and feedback. Digital Earth Africa thanks the Digital Earth Africa Product Development task team for the co-design, the co-development and early feedback on the Service.

Citation

text
Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia
Coastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268
js
var shoreline_annual = ee.FeatureCollection("projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_shorelines_annual");
var hotspot_zoom_1 = ee.FeatureCollection("projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_1");
var hotspot_zoom_2 = ee.FeatureCollection("projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_2");
var hotspot_zoom_3 = ee.FeatureCollection("projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_hotspots_zoom_3");
var rate_of_change = ee.FeatureCollection("projects/sat-io/open-datasets/DEAF/COASTLINES/coastlines_v040_rates_of_change");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/DEAF-Shorlines-V040

License

These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.

Created by: Digital Earth Africa

Curated in GEE by : Samapriya Roy

Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction, subpixel waterlines, coastal change, DEAF CoastLines, coastline data, coastal erosion

Last updated : 2023-03-26

Projects Delta Dtm (`docs/projects/delta_dtm.md`)

DeltaDTM Global coastal digital terrain model

DeltaDTM is a global coastal Digital Terrain Model (DTM) with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.43 m overall. It improves upon the accuracy of existing global elevation datasets by correcting Copernicus DEM with spaceborne lidar data from the ICESat-2 and GEDI missions. This correction process involves bias correction, filtering of non-terrain cells (e.g., vegetation and buildings), and gap filling using interpolation. DeltaDTM specifically focuses on low-lying coastal areas (below 30 m above Mean Sea Level) that are particularly vulnerable to sea-level rise, subsidence, and extreme weather events.

DeltaDTM is a valuable resource for a wide range of applications, including coastal management, flood modeling, and adaptation planning. Its improved accuracy enables more precise assessments of coastal flood risks and supports the development of effective mitigation and adaptation strategies. The dataset is freely available in the public domain and can be easily accessed and utilized by researchers, policymakers, and coastal communities. You can read the paper here and download the dataset here.

Citation

text
Pronk, M., Hooijer, A., Eilander, D. et al. DeltaDTM: A global coastal digital terrain model. Sci Data 11, 273 (2024).
https://doi.org/10.1038/s41597-024-03091-9

Dataset Citation

text
Pronk, Maarten (2024): DeltaDTM: A global coastal digital terrain model. Version 3. 4TU.ResearchData. dataset.
https://doi.org/10.4121/21997565.v3

Earth Engine Snippet

js
var delta_dtm = ee.Image("projects/sat-io/open-datasets/DELTARES/deltadtm_v1-1");
var elevation = delta_dtm.select('b1');
elevation = elevation.updateMask(elevation.neq(30)); // Dataset is clipped at 30m.

//Setup basemaps
var snazzy = require("users/aazuspan/snazzy:styles");
snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale");

var elevationVis = {
  min: 0,
  max: 30.0,
  // cmocean deep
  palette: ["281a2c", "3f396c", "3e6495", "488e9e", "5dbaa4", "a5dfa7", "fdfecc"]
};

Map.setCenter(103, 0, 7);  // South East Asia
Map.addLayer(elevation, elevationVis, 'DeltaDTM');

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/DELTA-DTM

License

DeltaDTM is licensed as CC-BY 4.0. DeltaDTM is produced using Copernicus WorldDEM-30 Β© DLR e.V. 2010-2014 and Β© Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.

Created by: Deltares, Pronk, M., Hooijer, A., Eilander, D. et al 2024

Curated in GEE by: Maarten Pronk and Samapriya Roy

Keywords: Altimetry, Digital Elevation Model (DEM), Digital terrain model (DTM), elevation, GEDI, ICESat-2, LiDAR

Last updated in GEE: 2024-11-18

Projects Dynqual (`docs/projects/dynqual.md`)

DynQual Global Surface Water Quality Dataset

Maintaining optimal surface water quality is essential for preserving ecosystems and ensuring safe human water utilization. However, our understanding of surface water quality relies heavily on data from monitoring stations, which are spatially limited and temporally fragmented. Addressing these limitations, we introduce the dynamical surface water quality model (DynQual). This model offers simulations of water temperature (Tw), as well as concentrations of total dissolved solids (TDS), biological oxygen demand (BOD), and fecal coliform (FC). DynQual operates at a daily time step and boasts a spatial resolution of 5 arcmin (βˆΌβ€‰10 km).

This comprehensive global model allows us to evaluate its performance against real-world water quality observations. In addition, we present insights into spatial patterns and temporal trends of TDS, BOD, and FC concentrations spanning the years 1980 to 2019. Our analysis identifies dominant sectors contributing to surface water pollution. Remarkably, DynQual reveals widespread multi-pollutant hotspots, particularly in northern India and eastern China, though water quality issues extend across all regions worldwide. The most significant declines in water quality have occurred in developing regions, especially sub-Saharan Africa and South Asia. Researchers can access the open-source model code (Jones et al., 2023) as well as the global datasets encompassing simulated hydrology, Tw, TDS, BOD, and FC at 5 arcmin resolution on a monthly basis (Jones et al., 2022b). These datasets hold the potential to enhance diverse studies ranging from ecological research to human health and water scarcity assessments. Discover more at DynQual Model Code and Global Surface Water Quality Datasets. You can read the full paper here

The constituents, such as total dissolved solids (TDS), biological oxygen demand (BOD), and fecal coliform (FC), are concentrations. The routed versions of these constituents (routed_TDS, routed_BOD, and routed_FC) are masses of pollutants. These values represent the pollution without considering dilution, and therefore more accurately reflect the transport or "export" of pollution through the river network.

Please refer to the original paper on guidance on displaying the concentration maps, the authors recommend these are only plotted above a given discharge/channelStorage threshold

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Datasets preprocessing

The datasets were downloaded and converted from NetCDF to Geotiff format for ingestion. Since this was a multi band monthly aggregated image and I wanted to allow the user to slice by time frame, the image bands were seperated as individual images and the overall results are image collections with date range information attached.

Citation

text
Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.:
DynQual v1.0: a high-resolution global surface water quality model, Geosci. Model Dev., 16, 4481–4500, https://doi.
org/10.5194/gmd-16-4481-2023, 2023.

Dataset citation

text
Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., & van Vliet, M. T. H.
(2022). Global monthly hydrology and water quality datasets, derived from the dynamical surface water quality
model (DynQual) at 10 km spatial resolution [Data set]. In Geoscientific Model Development (Vol. 16, pp.
4481–4500). Zenodo. https://doi.org/10.5281/zenodo.7139222

Earth Engine Snippet

js
var fc = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/fecal-coliform");
var routed_fc = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/routed-fc");
var discharge = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/discharge");
var storage = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/channel-storage");
var avg_annualDischarge = ee.Image("projects/sat-io/open-datasets/DYNQUAL/discharge-avg-annual");
var tds = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/total-dissolved-solids");
var routed_tds = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/routed-tds");
var bod = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/biological-oxygen-demand");
var routed_bod = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/routed-bod");
var water_temp = ee.ImageCollection("projects/sat-io/open-datasets/DYNQUAL/water-temperature");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/DYNQUAL-EXAMPLE

License

Creative Commons Attribution 4.0 International Public License

Created by: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.

Curated in GEE by: Samapriya Roy

Keywords: water quality, discharge, water temperature, total dissolved solids, TDS, salinity, biological oxygen demand, BOD, fecal coliform, FC

Projects Edge Matched (`docs/projects/edge_matched.md`)

Edge-matched Global, Subnational and operational Boundaries

Global Edge-matched Subnational Boundaries: Humanitarian Edge Matched

Uses OCHA Common Operational Datasets (COD) when available, falling back to geoBoundaries for regions without coverage. Represents the latest available data for humanitarian operational use. Uses the OpenStreetMap International ADM0 worldview for edge-matching. You can find dataset links in different formats here

License

The humanitaring edge matched datasets are under a Open Data Commons Open Database License (ODbL) license.

Attribution

FieldMaps, OCHA, geoBoundaries, U.S. Department of State, OpenStreetMap

Earth Engine Snippet

js
var adm1 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm1_polygons");
var adm2 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm2_polygons");
var adm3 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm3_polygons");
var adm4 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-humanitarian/adm4_polygons");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/HUMANITARIAN-EDGEMATCHED

Global Edge-matched Subnational Boundaries: Open Edge Matched

Uses geoBoundaries exclusively to ensure all data comes from sources with clearly defined licenses. Suitable for academic or commercial use. Uses the U.S. Geological Survey International ADM0 worldview for edge-matching. You can find dataset links in different formats here

License

The open datasets are under a Creative Commons Attribution 4.0 International (CC BY 4.0) and any derived work must include attribution.

Attribution

FieldMaps, geoBoundaries, U.S. Department of State, U.S. Geological Survey

Earth Engine Snippet

js
var adm1 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm1_polygons");
var adm2 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm2_polygons");
var adm3 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm3_polygons");
var adm4 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-open/adm4_polygons");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/OPEN-EDGEMATCHED

International ADM0 boundaries

International boundaries are built using either OpenStreetMap or U.S. Geological Survey coastlines. Within each set, ADM0 layers comes in several versions to represent different world views of disputed areas. Starting with the "All" version, areas are dissolved together based on varying international recognition. Only international boundaries were ingested, where the default version, uses OpenStreetMap (download) for coastlines so that it aligns with web maps using OSM for basemaps or other data. Specialty version, uses U.S. Geological Survey (download) for coastlines so that intellectual property and related rights in this dataset are absent.

License

The OSM edge matched datasets are under a Open Data Commons Open Database License (ODbL) license while the USGS datasets are under a CC0 or public domain license.

Attribution

The OSM datasets have attribution keywords defined such as
FieldMaps, U.S. Department of State, OpenStreetMap

Earth Engine Snippet

js
var adm0_osm = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/field-maps/OSM_adm0_polygons");
var adm0_usgs = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/field-maps/USGS_adm0_polygons");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/AMD0-EDGEMATCHED

Common Operation dataset Edge Matched

The COD layers are obtained from the Humanitarian Data Exchange at the source URLs below before processed for edge matching. Extended layers can be downloaded and clipped to any ADM0. You can download the dataset in different formats here. Datasets were merged to create singular representation at each hierarchy so all ADM1 for example and ADM2 for example. Each component of the dataset retains the original license provided.

Earth Engine Snippet

js
var adm1 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm1_cod");
var adm2 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm2_cod");
var adm3 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm3_cod");
var adm4 = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-cod/adm4_cod");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/COD-EDGEMATCHED

GeoBoundaries Edge matched

The Humanitarian Data Exchange at the source URLs below before processed for edge matching. Extended layers can be downloaded and clipped to any ADM0. You can download the dataset and find license information heredifferent formats here. Datasets were merged to create singular representation at each hierarchy so all ADM1 for example and ADM2 for example. Each component of the dataset retains the original license provided.

js
var adm = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm1_geoboundaries");
var adm = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm2_geoboundaries");
var adm = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm3_geoboundaries");
var adm = ee.FeatureCollection("projects/sat-io/open-datasets/field-maps/edge-matched-geoboundaries/adm4_geoboundaries");

sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GEOBOUNDARIES-EDGEMATCHED

Curated in GEE by : Samapriya Roy

Keywords: FieldMaps, U.S. Department of State, OpenStreetMap,U.S. Geological Survey, geoboundaries

Last updated on GEE: 2022-10-30

Projects Elc (`docs/projects/elc.md`)

Continental-scale land cover mapping at 10 m resolution over Europe

A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018. You can read the preprint here

The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).

Citation

text
Venter, Zander S., and Markus AK Sydenham. "Continental-scale land cover mapping at 10 m resolution
over Europe (ELC10)." arXiv preprint arXiv:2104.10922 (2021).

Earth Engine Snippet

js
var elc10= ee.ImageCollection("projects/sat-io/open-datasets/NINA/ELC10")

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/EUROPE-10m-LULC

Category Land Cover Class Hex Code
1 Artificial land #CC0303
2 Cropland #CDB400
3 Woodland #235123
4 Shrubland #B76124
5 Grassland #92AF1F
6 Bare land #F7E174
7 Water/permanent snow/ice #2019A4
8 Wetland #AEC3D6

Dataset Citation

text
Venter, Zander S., & Sydenham, Markus A.K. (2020). ELC10: European 10 m resolution land cover map 2018
(Version 01) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4407051

License

Creative Commons Attribution-Share Alike 4.0 International License

Created by: Venter, Zander S., & Sydenham, Markus A.K.

Curated by: Samapriya Roy

Keywords: : land use, europe, land cover, remote sensing, copernicus, sentinel, satellite

Last updated: 2021-04-29

Projects Elc Gdp (`docs/projects/elc_gdp.md`)

Global Electric Consumption revised GDP

In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data.

Gridded population and nighttime light data are the most popular proxy tools, and have been adopted extensively because of their strong correlation with economic output and electricity use. Finally the authors note that although nighttime light data as a single indicator may ignore factors such as value added or reduced by forestry or desertification, it is still an effective proxy for calibrating economic growth.You can read the paper here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Paper Citation

text
Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., & Liu, Y. (2022). Global 1 kmΓ— 1 km gridded revised real gross domestic product and
electricity consumption during 1992–2019 based on calibrated nighttime light data. Scientific Data, 9(1), 1-14. https://doi.org/10.1038/
s41597-022-01322-5

Data Citation

text
Chen, Jiandong; Gao, Ming (2021): Global 1 km Γ— 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based
on calibrated nighttime light data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.17004523.v1

Dataset units

  • GDP: Millions of 2017 US dollars
  • Electricity consumption: Kilowatt hours

Earth Engine Snippet: GRIDDED Electricity Consumption

js
var global_ec = ee.ImageCollection("projects/sat-io/open-datasets/GRIDDED_EC");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION

Earth Engine Snippet: GRIDDED GDP based on Electricity Consumption

js
var global_elc_gdp = ee.ImageCollection("projects/sat-io/open-datasets/GRIDDED_EC-GDP");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:population-socioeconomics/GRIDDED-ELECTRICITY-CONSUMPTION-GDP

License

This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Curated in GEE by: Samapriya Roy

Keywords: GDP, Electricity Consumption, Night Lights

Last updated: 2022-09-27

Projects Electric Grid (`docs/projects/electric_grid.md`)

Facebook Electrical Distribution Grid Maps

Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D’Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available.

Building Electrical Grid Maps begins by taking monthly images from the VIIRS satellite, and creating a composite. We then apply a custom image processing filter to remove background and reflected light, and identify locations that consistently demonstrate night-time lighting. These then serve as a proxy for the existence of grid electricity. Using known electrical grids as templates based on data available from energydata.info, we employ a custom algorithm to connect the communities and infer grid paths based on their likelihood to follow roads, avoid water, and follow the shortest paths possible. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra

Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again.

Earth Engine Snippet

js
var gmv_raster = ee.ImageCollection("projects/sat-io/open-datasets/facebook/global_medium_voltage_grid")
var gmv_vector = ee.FeatureCollection("projects/sat-io/open-datasets/facebook/gmv_grid");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/FACEBOOK-ELECTRICAL-DIST-GRID-MAPS

Resolution:

geotiff is provided at Bing Tile Level 20

Location

CΓ΄te d'Ivoire, Democratic Republic of the Congo, Malawi, Nigeria, Uganda, Zambia

License

This work is distributed under the Creative Commons Attribution 4.0 License.

Curated by: Samapriya Roy

Keywords: Electrical Distribution Grid, Facebook, Ivory Coast, Democratic Republic of the Congo, Malawi, Nigeria, Uganda, Zambia

Last updated: 2021-04-17

Projects Emc Built (`docs/projects/emc_built.md`)

Copernicus EMC-BUILT Global Built-up Surface R2025A

The Copernicus Exposure Mapping Component (EMC) Built-up product (EMC-BUILT R2025A) provides high-resolution global information on built-up surfaces, non-residential structures, and vegetation within built-up areas for the reference year 2022. This dataset represents the first operational Copernicus production of built-up information based on Sentinel-2 data and deep learning architecture, ensuring continuity with previous Global Human Settlement Layer (GHSL) products while introducing major methodological innovations. The dataset is produced by the European Commission's Joint Research Centre (JRC) as part of the Copernicus Emergency Management Service (CEMS) to support crisis management, disaster risk reduction, urban planning, and sustainable development monitoring.

The production employs a modified U-Net convolutional neural network (CNN) trained on Sentinel-2 imagery with comprehensive geospatial covariates including spectral metrics, temporal harmonics, and building footprints from multiple open sources (OpenStreetMap, Microsoft Building Footprints, Google Open Buildings). The dataset provides three core products at multiple resolutions (10m, 100m, 1000m in World Mollweide projection, and 3/30 arc-seconds in WGS84): total built-up surface, non-residential built-up surface, and greenness fraction within the built-up domain. All products follow an open and free data access policy aligned with the Copernicus programme principles.

Methodology Overview

The EMC-BUILT production workflow consists of six main steps:

  1. Pre-processing: Computation of spectral and temporal metrics from all Sentinel-2 Level-1C scenes for 2022, including harmonic coefficients and percentile composites
  2. Reference Data Collection: Compilation of global building footprint data from multiple open sources (OSM, Microsoft, Google)
  3. Model Training: Development of a modified U-Net CNN to map Sentinel-2 metrics to built-up surfaces
  4. Prediction: Application of trained models across all Sentinel-2 tiles with ensemble predictions
  5. Post-processing: Ensemble averaging, commission error correction using auxiliary indices (NDWI), and value rescaling
  6. Mosaicking and Reprojection: Assembly and reprojection to global grids in Mollweide and WGS84

The greenness layer is derived from median NDVI values with a 300m buffer around built-up areas to capture vegetation in and around settlements.

!!! info
EMC-BUILT and GHSL R2023 are incompatible data streams. Direct temporal comparisons or change detection between these releases are not supported due to substantial methodological differences.

Dataset Characteristics

Parameter Value
Spatial Resolution 10m, 100m, 1000m (Mollweide); 3 arcsec, 30 arcsec (WGS84)
Temporal Coverage 2022 (reference year)
Geographic Coverage Global land areas (βˆ’60.9Β° to 89.1Β° latitude)
Coordinate Systems World Mollweide (ESRI:54009), WGS84 (EPSG:4326)
Data Format Cloud-optimised GeoTIFF (COG)
Source Data Sentinel-2 Level-1C imagery
Methodology U-Net CNN with Random Forest dasymetric approach
Update Frequency Biennial (operational production every 2 years)

Available Data Products

The EMC-BUILT collection provides three complementary datasets:

Asset Name Description Value Range Units
EMC-BUILT-S Total built-up surface area per grid cell 0-100 (10m), 0-10,000 (100m), 0-1,000,000 (1000m) Square metres
EMC-BUILT-NRES Non-residential built-up surface (industrial/commercial) 0-100 (10m), 0-10,000 (100m), 0-1,000,000 (1000m) Square metres
EMC-BUILT-GREENNESS Vegetation fraction (NDVI) within and around built-up areas 0-100 NDVI percentage

Citation

Dataset

text
Riffler, M., Leitner, A., Ralser, S., Poglitsch, E., Schleicher, C., Faucqueur, L., Pennec, A., Marconcini, M., Esch, T., Julea, A.M., Kemper, T., Politis, P., Melchiorri, M. (2025): EMC-BUILT R2025A – Exposure Mapping Component built-up surface grid, derived from Sentinel-2 year 2022. European Commission, Joint Research Centre (JRC) [Dataset]
doi: 10.2905/4cd8ccee-bfd1-48c5-9047-61c0343c41e9

Concept & Methodology:

text
Kemper, T., Politis, P., Mari Rivero, I., Riffler, M., Leitner, A. et al. (2025): Copernicus Exposure Mapping Component Built-up product 2022 - EMC-BUILT R2025,
Publications Office of the European Union, Luxembourg, JRC144315

Earth Engine Snippet

javascript
// Load the three EMC-BUILT products
var builtUpSurface = ee.ImageCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/ESA/CEMS/EMC-BUILT-S");
var nonResidential = ee.ImageCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/ESA/CEMS/EMC-BUILT-NRES");
var greenness = ee.ImageCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/ESA/CEMS/EMC-BUILT-GREENNESS");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/EMC-BUILT-2025A

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). The reuse policy is implemented under European Commission Decision 2011/833/EU. All data and methods follow a fully open and free access policy aligned with the Copernicus programme and GHSL principles.

Provided by: European Commission Joint Research Centre (JRC), Copernicus Emergency Management Service

Keywords: Built-up surface, Urban mapping, Sentinel-2, Deep learning, U-Net, Settlement mapping, Non-residential buildings, Urban greenness, GHSL, Copernicus, Exposure mapping, Disaster risk reduction, Global Human Settlement Layer, Land cover

Curated in GEE by: Samapriya Roy

Last updated in GEE: 2025-11-11

Projects Energy Farms (`docs/projects/energy_farms.md`)

Harmonised global datasets of wind and solar farm locations and power

Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet’s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.

Data is available for download from figshare here

Data Citation

text
Dunnett, S. Harmonised global datasets of wind and solar farm locations and power. figshare. Dataset. https://doi.org/10.6084/m9.figshare.11310269 (2020)

Paper Citation

text
Dunnett, S., Sorichetta, A., Taylor, G. et al. Harmonised global datasets of wind and solar farm locations and power. Sci Data 7, 130 (2020). https://doi.org/10.1038/s41597-020-0469-8

Earth Engine Snippet

js
var wind_farms = ee.FeatureCollection("projects/sat-io/open-datasets/global_wind_farms_2020");
var solar_farms = ee.FeatureCollection("projects/sat-io/open-datasets/global_solar_farms_2020");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/HARMONIZED-WIND-SOLAR-FARMS

Property Mapping

Name Detail
X X coordinate (.csv only)
Y Y coordinate (.csv only)
x_id unique identifier for data record
GID_0 ISO3 country code
panels number of panels
turbines number of turbines
panel.area total panel area in km2Β (p_areaΒ for .gdb files)
landscape.area landscape area in km2Β (l_areaΒ for .gdb files)
water binary response indicating whether data record is classified as water
urban binary response indicating whether data record is classified as urban centre
power estimated power capacity in MW

License

Data adapted or built on OpenStreetMap data are required to be distributed under the same licence. These data are therefore made available under the Open Data Commons Open Database License (ODbL). Personal figshare accounts cannot currently present data under this licence so the data are currently (incorrectly) presented under a CC0 licence as a stopgap until this changes.

Created by: Dunnett et al.

Curated by: Samapriya Roy

Keywords: solar, wind, energy, renewable

Last updated: 2021-08-31

Projects Eog Viirs Ntl (`docs/projects/eog_viirs_ntl.md`)

EOG Annual VIIRS Night Time Light

???+ note

text
**This dataset is currently only available to those in the [insiders program](https://gee-community-catalog.org/insiders/)**

A new consistently processed time series of annual global VIIRS nighttime lights has been produced from monthly cloud-free average radiance grids spanning 2012 to 2021 is processed by the Earth Observation Group(EOG). The new methodology (version 2.1) is a modification of the original VNL V1 that is available in the Earth Engine catalogue. Compared to VNL V1, this improved VNL V2.1 version removes ephemeral lights and background noise.

In both methods there is an initial filtering to remove sunlit, moonlit and cloudy pixels, leading to rough composites that contains lights, fires, aurora and background. In the original method, the rough annual composites are made from a full year of nightly DNB data. In the new method, the rough composites are made on monthly increments and then combined to form rough annual composites. Both methods employ outlier removal to discard biomass burning pixels and isolate the background.

In the original method the outlier removal is performed on scattergrams generated for each 15 arc second grid cell, with outliers clipped off from both the high and low radiance sides of the scattergram. The discard of outlier pixels proceeds until the scattergram’s standard deviation stabilizes. The new method uses the twelve-month median radiance to discard high and low radiance outliers, filtering out most fires and isolating the background. Background areas are zeroed out in both methods using the data range (DR) calculated from 3x3 grid cells. In both methods, the DR threshold for background is indexed to cloud-cover levels, with higher DR thresholds in areas having low numbers of cloud-free coverages. In the new method, particular attention is given to setting a single DR threshold for distinguishing lit grid cells from background for each 15 arc second grid cell. This is achieved by setting the DR threshold from a multiyear maximum median and a corresponding multiyear percent cloud-cover grids. The multiyear approach makes it possible to detect lighting present in each 15 arc second grid cell with a single DR threshold across all the years in the series. You can get additional information here and download v2.1 here

Disclaimer: Whole or parts of the dataset description were provided by the author(s) or their works.

Data preprocessing

Datasets were preprocessed and metadata was added to each image in collection. The year 2012 had two datasets for a varying period and as such was excluded from the collection for now and maybe added later.

Citation

text
Elvidge, C.D, Zhizhin, M., Ghosh T., Hsu FC, Taneja J. Annual time series of global VIIRS nighttime lights derived from monthly averages:2012 to
2019. Remote Sensing 2021, 13(5), p.922, https://doi.org/10.3390/rs13050922

Code Snippet

js
var average = ee.ImageCollection("projects/sat-io/open-datasets/EOG_VNL_V21/average");
var maximum = ee.ImageCollection("projects/sat-io/open-datasets/EOG_VNL_V21/maximum");
var median = ee.ImageCollection("projects/sat-io/open-datasets/EOG_VNL_V21/median");
var minimum = ee.ImageCollection("projects/sat-io/open-datasets/EOG_VNL_V21/minimum");
var average_masked = ee.ImageCollection("projects/sat-io/open-datasets/EOG_VNL_V21/average_masked");
var median_masked = ee.ImageCollection("projects/sat-io/open-datasets/EOG_VNL_V21/median_masked");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/EOG-VNL-V21

License

Public domain license with properitary license language.

text
Colorado School of Mines data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no
restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and
is being provided without restriction on use and distribution.

Provided by: Colorado School of Mines, Elvidge et al. 2019

Curated in GEE by : Samapriya Roy

keywords: Nighttime lights, VIIRS, Annual time series, Earth Observation Group , EOG, Colorado School of Mines

Last updated on GEE: 2023-01-28

Projects Era5 Heat (`docs/projects/era5_heat.md`)

ERA5-HEAT Dataset

This dataset provides a complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor
conditions. This dataset, also known as ERA5-HEAT (Human thErmAl comforT) represents the current state-of-the-art for bioclimatology data record
production. The dataset is organized around two main variables: 1) the mean radiant temperature (MRT) and 2) the universal thermal climate index
(UTCI) These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and
radiation.

The dataset is computed using the ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF). ERA5 combines model data with
observations from across the world to provide a globally complete and consistent description of the Earth’s climate and its evolution in recent
decades. ERA5 is regarded as a good proxy for observed atmospheric conditions. Additional external information is available on this product here.

Dataset Description

Spatial Information

Attribute Details
Spatial extent Global
Spatial resolution 27.75km (.25 deg)
Temporal resolution Daily
Time span 1940-01-01 to present
Update frequency Updated daily with lag of 2 weeks

Variables

Variable Units Scale factor Description
Mean Radiant Temperature (β€˜mrt_mean’, β€˜mrt_max’, β€˜mrt_min’, β€˜mrt_median’) Degrees Kelvin 1.0 Provided in four bands for daily mean, maximum, minimum, and median.
Universal Thermal Climate Index (β€˜utci_mean’, β€˜utci_max’, β€˜utci_min’, β€˜utci_median’) Degrees Kelvin 1.0 Provided in four bands for daily mean, maximum, minimum, and median.

Citation

text
Di Napoli C., Barnard C., Prudhomme C., Cloke HL and Pappenberger F. (2020): Thermal comfort indices derived from ERA5 reanalysis. Copernicus
Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.553b7518 (Accessed on DD-MMM-YYYY)

Earth Engine Snippet

js
// Read in Image Collection and get first image
var era5_heat_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-era5-heat')
var era5_heat_i = era5_heat_ic.first()

// Print first image to see bands
print(era5_heat_i)

// Visualize select bands from first image β€” additional bands are present in the Image Collection
var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse()
Map.addLayer(era5_heat_i.select('mrt_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Mean Radiant Temperature, Daily Mean')
Map.addLayer(era5_heat_i.select('utci_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Universal Thermal Climate Index, Daily Mean')

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/ERA5-HEAT

License

ECMWF is available under an open license with terms of agreement available here

Keywords: heat exposure, climate, reanalysis, global, era5, thermal, public health, climate engine

Provided by: Copernicus

Curated in GEE by: Climate Engine Org

Projects Esa Iq (`docs/projects/esa_iq.md`)

ESA WorldCover 10 m 2020 V100 InputQuality

The ESA WorldCover 10 m 2020 V100 product is delivered in 3x3 degree tiles as Cloud Optimized GeoTIFFs (COGs) in EPSG:4326 projection (geographic latitude/longitude CRS). There are 2651 tiles and more information on accessing this dataset can be found here. The current collection focused on the Input Quality layers only, the Map layer is available in Google Earth Engine as an image collection.

The input quality layer is a per pixel quality indicator showing the quality of the Earth Observation (EO) input data. The layer is a 3 band GeoTIFF with

β€’ Band 1: Number of Sentinel-1 GAMMA0 observations used in the classification workflow

β€’ Band 2: Number of Sentinel-2 L2A observations used in the classification workflow

β€’ Band 3 Percentage (0-100) of invalid S2 observations discarded in the classification workflow
(after cloud and cloud shadow filtering).

Combining Band 2 and Band 3 yields the total absolute number of valid Sentinel-2 L2A observations
used in the classification workflow.

License

The ESA WorldCover product is provided free of charge, without restriction of use. For the full license information see the Creative Commons Attribution 4.0 International License.

Publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.

Citation

text
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A.,
Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li,
Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100.
https://doi.org/10.5281/zenodo.5571936

Earth Engine Snippet

js
var esa_iq = ee.ImageCollection("projects/sat-io/open-datasets/ESA_WorldCover_Input_Quality");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/ESA-10m-WORLDCOVER-IQ

Data access page: ESA_WorldCover_v100

Provided by: Zanaga et al, ESA WorldCover consortium

Curated in GEE by: Samapriya Roy

Keywords: : land, cover, land use, land cover, lulc, 10m, global, world, sentinel-1, sentinel 2, ESA

Last updated: 2021-11-01

Projects Esrilc2020 (`docs/projects/esrilc2020.md`)

ESRI 2020 Global Land Use Land Cover from Sentinel-2

This layer displays a global map of land use/land cover (LULC). The map is derived from ESA Sentinel-2 imagery at 10m resolution. It is a composite of LULC predictions for 10 classes throughout the year in order to generate a representative snapshot of 2020. This map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.

The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map of 2020.

Processing platform
Sentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.

You can find more information here
Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail

Citation

text
Karra, Kontgis, et al. β€œGlobal land use/land cover with Sentinel-2 and deep learning.”
IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.

Class definitions

  1. Water
    Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.

  2. Trees
    Any significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).

  3. Grass
    Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures.

  4. Flooded vegetation
    Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.

  5. Crops
    Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.

  6. Scrub/shrub
    Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants

  7. Built Area
    Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.

  8. Bare ground
    Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.

  9. Snow/Ice
    Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.

  10. Clouds
    No land cover information due to persistent cloud cover.

For Accuracy Assessment information visit the ESRI release page

Category Land Cover Class Hex Code
1 No Data #FFFFFF
2 Water #1A5BAB
3 Trees #358221
4 Grass #A7D282
5 Flooded Vegetation #87D19E
6 Crops #FFDB5C
7 Scrub/Shrub #EECFA8
8 Built Area #ED022A
9 Bare Ground #EDE9E4
10 Snow/Ice #F2FAFF
11 Clouds #C8C8C8

Earth Engine Snippet

js
var esri_lulc2020= ee.ImageCollection("projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m")

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/ESRI-LULC-2020

Acknowledgements

Training data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Credits, Attributions and License

This dataset was produced by Impact Observatory for Esri. Β© 2021 Esri. This dataset is available under a Creative Commons BY-4.0 license and any copy of or work based on this dataset requires the following attribution:

text
This dataset is based on the dataset produced for the Dynamic World Project
by National Geographic Society in partnership with Google and the World Resources Institute.

Data download page: Esri 2020 Land Cover Downloader

Curated in GEE by: Samapriya Roy

Keywords: : land, cover, land use, land cover, lulc, 10m, global, world, sentinel, sentinel-2, sentinel 2, impact observatory, impact, 2020, deep learning

Last updated: 2021-06-25

Projects Et0 (`docs/projects/et0.md`)

Global Reference Evapotranspiration Layers

The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 3 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.1 at 30 arc seconds or ~ 1km at the equator. You can read the paper here.

Potential Evapo-Transpiration (PET) is a measure of the ability of the atmosphere to remove water through Evapo-Transpiration (ET) processes. Among several equations to estimate PET, a FAO application of the Penman-Monteith equation (Allen et al. 1998), here referred as FAOPM, is currently widely considered as a standard method (Walter et al. 2000). The FAO introduced the definition of PET as the ET of a reference crop (ET0) under optimal conditions, having the characteristics of well-watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70 seconds per meter and an albedo of 0.23 (Allen et al. 1998). The FAO-PM is a predominately physically based approach, which can be used globally because it does not require estimations of additional site-specific parameters. However, a major drawback of the FAO-PM method is its relatively high need for specific data for a variety of parameters
(i.e. windspeed, relative humidity, solar radiation, etc.).

Data citation

text
Zomer, Robert; Trabucco, Antonio (2019): Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.
figshare. Dataset. https://doi.org/10.6084/m9.figshare.7504448.v6

Paper citation

text
Zomer, R.J.; Xu, J.;  Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database.
Scientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1

Global-ET0 grid layers are available as monthly averages (12 data layers, i.e. one layer for each month) or as an annual average (1 data layer) as well as standard deviation for annual average for the 1970-2000 period.

Earth Engine Snippet

js
var et_monthly = ee.ImageCollection("projects/sat-io/open-datasets/global_et0/global_et0_monthly");
var et_yearly = ee.Image("projects/sat-io/open-datasets/global_et0/global_et0_yearly");
var et_yearly_sd = ee.Image("projects/sat-io/open-datasets/global_et0/global_et0_yearly_sd");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/GLOBAL-ET0

License

The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use under the CC BY 4.0 Attribution 4.0 International license.

Data Website: You can download the data and description here

Curated in GEE by: Samapriya Roy

Keywords: aridity index, evapotranspiration, geospatial modeling

Last updated: 2022-09-02

Projects Fabdem (`docs/projects/fabdem.md`)

FABDEM (Forest And Buildings removed Copernicus 30m DEM)

FABDEM (Forest And Buildings removed Copernicus DEM) removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM) (Airbus, 2020). The data is available at 1 arc second grid spacing (approximately 30m at the equator) for the globe. The authors use machine learning to
remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∼30 m) grid spacing. You can read the paper here and the overall dataset can be downloaded here

Disclaimer: Parts or all of the dataset description is borrowed from existing description provided by authors.

Source Data structure

The data are in Geotiff format, with each file divided into 1x1 degree tiles. Files are divided into 10x10 degree zipped folders (detailed in Data structure section below). Files are labelled using the south-west corner of the tile. For example N51E005_FABDEM_V1-0.tif has an extent from 51-52 degrees N, 5-6 degrees E.

Zipped folders are labeled with the southwest corner to northeast corner. For example For example N10E010-N20E020_FABDEM_V1-0.zip has an extent from 10-20 degrees N, 10-20 degrees E.

Citation

text
Hawker, Laurence, Peter Uhe, Luntadila Paulo, Jeison Sosa, James Savage, Christopher Sampson, and Jeffrey Neal. "A 30m global map of elevation with
forests and buildings removed." Environmental Research Letters (2022).

Earth Engine Snippet

js
var fabdem = ee.ImageCollection("projects/sat-io/open-datasets/FABDEM");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:elevation-bathymetry/FABDEM

License

The FABDEM dataset is licensed under a Creative Commons "CC BY-NC-SA 4.0" license.

This is a non-commercial and ShareAlike license. In other words, FABDEM may not be used for commercial purposes, and if it is remixed, transformed or built upon you must redistribute your contributions under the same license.

When using the data, users must include the below statements, as per the requirement of the original license.

FABDEM is produced using Copernicus WorldDEM-30 Β© DLR e.V. 2010-2014 and Β© Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the Copernicus program by law or by delegation do not incur any liability for any use of the Copernicus WorldDEM-30

The original license can be found: https://docs.sentinel-hub.com/api/latest/static/files/data/dem/resources/license/License-COPDEM-30.pdf

Created by: Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., & Neal, J

Curated by: Samapriya Roy

Keywords: digital elevation model, bare-earth, terrain, remote sensing, machine learning

Changelog

  • Updated FABDEM V1-2, an update on FABDEM V1-0. Changes detailed in FABDEM-V1-2 Changelog.pdf. FABDEM (Forest And Buildings removed Copernicus DEM) is a global elevation map that removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM). The data is available at 1 arc second grid spacing (approximately 30m at the equator) for the globe.

Projects Fcm10r9 (`docs/projects/fcm10r9.md`)

Forest Communities Mapping with AlphaEarth Embeddings

!!! warning "Preprint Dataset"
This dataset is based on a preprint research paper that has not yet been peer reviewed. The methodology, data products, and interpretations may be updated as the paper proceeds through the peer review process. Please cite appropriately and check for updates.

This dataset represents a novel application of deep learning-based geospatial embeddings for ecological mapping, demonstrating how the AlphaEarth Foundations (AEF) model can transform forest community mapping by synthesizing petabytes of multi-source Earth observations into information-rich, 64-dimensional feature spaces. By combining AEF embeddingsβ€”which harmonize Sentinel-1/2, Landsat, GEDI, SAR, climate, and numerous other data sources with 22,658 Forest Inventory and Analysis (FIA) plots, this dataset delivers comprehensive forest community maps at 10-meter resolution across the Northeastern and Midwestern United States (U.S. Forest Service Region 9).

The resulting products cover over 65 million hectares of forest across 20 states and include community-constrained species composition for over 50 tree species, plus forest structure metrics (aboveground carbon, total basal area, and stem density). Critically, the AEF embeddings eliminate the need for manual feature engineering that has constrained previous forest mapping approaches, providing globally consistent, highly generalizable predictors that enable both k-nearest neighbor (kNN) imputation for species composition and Random Forest regression for structural attributes. The aboveground carbon estimates outperform four existing national and regional products, demonstrating the transformative potential of geospatial embed

Explore Related Technologies & Topics

View All 9,111+ Repositories β†’

Discover similar high-velocity repositories, agent skills, and OpenAPI specifications across the ecosystem.

#awesome-list Browse awesome-list catalog β†’ #catalog Browse catalog catalog β†’ #community-catalog Browse community-catalog catalog β†’ #community-datasets Browse community-datasets catalog β†’ #earth-engine Browse earth-engine catalog β†’ #geospatial Browse geospatial catalog β†’ ⚑ MCP Servers Model Context Protocol β†’ 🧠 Agent Skills Prompt & rule files β†’ πŸ”Œ OpenAPI Specs REST & Swagger schemas β†’