A curated list of Google Earth Engine resources. Please visit the Awesome-GEE GitHub repo if you want to contribute to this project.

• Earth Engine official websites
• Get Started
• Get Help
• JavaScript API
• Python API
• R
• QGIS
• GitHub Developers
• Twitter
• Apps
• Presentations
• Videos
• Projects
• Websites
• Datasets
• Papers
• Contributing
• License

• Official homepage
• JavaScript Code Editor
• API Documentation
• Data Catalog
• Timelapse
• Earth Engine Apps
• Blog
• Sign up
• Developer Forum
• Issue Tracker
• Earth Engine API on GitHub
• Google Earth Engine Community Tutorials

1. Sign up for an Earth Engine account.
2. Read the Earth Engine API documentation - Get Started with Earth Engine.
3. Read another Earth Engine API documentation - Client vs. Server. Make sure you have a good understanding of client-side objects vs server-side objects.
4. Try out the JavaScript API or Python API (e.g., geemap).
5. Read Coding Best Practices.

• Earth Engine Developer Forum
• GIS Stack Exchange
• Report a bug
• Dataset requests
• Feature requests
• Slack channel for geemap and Earth Engine

• JavaScript Code Editor - The official Google Earth Engine JavaScript Code Editor.

• jdbcode/Snazzy-EE-TS-GIF - Apps for creating Landsat time series animations.
• fitoprincipe/geetools-code-editor - A set of tools to use in Google Earth Engine JavaScript Code Editor.
• Fernerkundung/EarthEngine_scripts - Scripts and snippets for Google Earth Engine.
• Google Earth Engine Toolbox (GEET) - Library to write small EE apps or big/complex apps with a lot less code.
• LandTrendr - Spectral-temporal segmentation algorithm.
• zecojls/tagee - Terrain Analysis in Google Earth Engine (TAGEE).

• Introduction to Google Earth Engine
• Introduction to JavaScript for Earth Engine
• Introduction to the Earth Engine JavaScript API
• Global Forest Change Analysis
• Global Surface Water Change Analysis
• Beginner's Cookbook
• Combining FeatureCollections
• Customizing Base Map Styles
• Forest Cover and Loss Estimation
• Getting Started with Drawing Tools
• Identifying Annual First Day of No Snow Cover
• Interactive Region Reduction App
• Land Surface Temperature in Uganda
• Landsat ETM+ to OLI Harmonization
• MODIS NDVI Times Series Animation
• Non-parametric trend analysis
• GEE 开发 on 知乎 by 无形的风
• Calculating Area in Google Earth Engine
• Extracting Time Series using Google Earth Engine
• Histogram Matching in Google Earth Engine
• Getting Git Right on Google Earth Engine
• AmericaView - Google Earth Engine (GEE) tutorials
• Earth Lab - Introduction to the Google Earth Engine code editor
• Coding Club - Intro to the Google Earth Engine
• Global Snow Observatory - Google Earth Engine Tutorials
• GEARS - Getting started with Google Earth Engine
• An Introduction to Remote Sensing for Ecologists Using Google Earth Engine
• An introduction to Google Earth Engine

• Earth Engine Python API installation

• earthengine-api - The official Google Earth Engine Python API.
• geemap - A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.
• geeadd - Google Earth Engine Batch Asset Manager with Addons.
• geeup - Simple CLI for Google Earth Engine Uploads.
• cartoee - Publication quality maps using Earth Engine and Cartopy.
• gee_tools - A set of tools for working with Google Earth Engine Python API.
• landsat-extract-gee - Get Landsat surface reflectance time-series from google earth engine.
• Ndvi2Gif - Creating seasonal NDVI compositions GIFs.

• earthengine-py-notebooks - A collection of 360+ Jupyter notebook examples for using Google Earth Engine with interactive mapping.
• earthengine-py-examples - A collection of 300+ examples for using Earth Engine and the geemap Python package.
• ee-tensorflow-notebooks - Repository to place example notebooks for Deep Learning applications with TensorFlow and Earth Engine.
• CoastSat - Global shoreline mapping tool from satellite imagery.
• Google-Earth-Engine-Python-Examples
• csaybar/EEwPython

• geemap and Earth Engine Python API tutorials
• A Quick Introduction to Google Earth Engine
• Google Earth Engine (GEE) and Image Analysis
• Earth Engine Python API Colab Setup
• Earth Engine TensorFlow demonstration notebook
• Earth Lab - Calculating the area of polygons in Google Earth Engine

• rgee - An R package for using Google Earth Engine.
• earthEngineGrabR - Simplify the acquisition of remote sensing data.

• rgee-examples - A collection of 250+ examples for using Google Earth Engine with R.

• rgee tutorial #1: Creating global land surface temperature maps
• rgee tutorial #2: Satellite image processing

• Earth Engine QGIS Plugin (Website, GitHub) - Integrates Google Earth Engine and QGIS using Python API.

• qgis-earthengine-examples - A collection of 300+ Python examples for using Google Earth Engine in QGIS.

• Creating Maps with Google Earth Engine

• earthengine-api
• Google Earth Engine Community
• Google Earth Engine Community Tutorials

• Cesar Aybar
• Justin Braaten
• Tirthankar "TC" Chakraborty
• Diego Garcia Diaz
• Gennadii Donchyts
• Ujaval Gandhi
• Philipp Gärtner
• Eduardo Lacerda
• Kel Markert
• Keiko Nomura
• Rodrigo E. Principe
• Samapriya Roy
• Sabrina Szeto
• Qiusheng Wu

• Earth Engine Bot
• Geospatial Python
• Synthetic Aperture Random

• Google Earth
• Google Earth Outreach
• Tyler Erickson
• Rebecca Moore
• Kurt Schwehr

• Cesar Aybar
• Justin Braaten
• Tirthankar "TC" Chakraborty
• Morgan Crowley
• Diego Garcia Diaz
• Gennadii Donchyts
• Ujaval Gandhi
• Philipp Gärtner
• Belize GEO
• Kel Markert
• Keiko Nomura
• Samapriya Roy
• Sabrina Szeto
• Dave Thau
• Qiusheng Wu

• Earth Engine Apps - Google
• An image gallery of almost all publicly available Google Earth Engine Apps - Philipp Gärtner
• A searchable list of all publicly available Google Earth Engine Apps

• Using the geemap Python package for interactive mapping with Earth Engine - Earth Engine Virtual Meetup on May 8, 2020
• Cloud computing and interactive mapping with Earth Engine and open-source GIS - GeoInsider webinar on May 28, 2020
• Mapping Wetland Inundation Dynamics using Google Earth Engine - Machine learning and data fusion workshop on June 10, 2020

• SERVIR Global - Introduction to Google Earth Engine

• Geo For Good 2019 on YouTube
• Earth Engine Video Tutorials

• Getting Started with Earth Engine with Sabrina Szeto (video - slides)
• Earth Engine Virtual Meetup on May 6, 2020 (video)

• geemap tutorials on YouTube
• geemap tutorials on 哔哩哔哩
• geemap tutorials on 西瓜视频
• GeoInsider webinar - Cloud computing and interactive mapping with Earth Engine and open-source GIS (video - slides)
• GeoInsider webinar 2 - Using Google Earth Engine for large-scale geospatial analysis: A case study of automated surface water mapping (video | slides)

• Google Earth Engine on Research Gate

• Global Surface Water Explorer
• Global Forest Cover Change
• Global Forest Watch
• Map Of Life
• Climate Engine
• Surface Water Mapping Tool
• Surface water changes (1985-2016)
• Decision Support Tools
• Earth Map
• CoastSat shoreline change database

• awesome-gee-community-datasets

• Landsat 8 Surface Reflectance
• Landsat 8 TOA Reflectance

• Sentinel-1 SAR GRD
• Sentinel-2 MSI

• NAIP: National Agriculture Imagery Program

• NLCD: USGS National Land Cover Database

• Aybar, C., Wu, Q., Bautista, L., Yali, R., & Barja, A. (2020). rgee: An R package for interacting with Google Earth Engine. The Journal of Open Source Software. 5(51), 2272. https://doi.org/10.21105/joss.02272
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
• Wu, Q. (2020). geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software. 5(51), 2305. https://doi.org/10.21105/joss.02305

• IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Cloud Computing in Google Earth Engine for Remote Sensing (Call for Papers)
• Remote Sensing, Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science (Call for Papers, Published Papers)
• Remote Sensning, Google Earth Engine Applications (Call for Papers, Published Papers)
• Remote Sensing of Environment, Remote Sensing of Land Change Science with Google Earth Engine (Call for Papers, Published Papers)

• Amani, M., Ghorbanian, A., Ahmadi, A., Kakooei, M., ..., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2020.3021052
• Kumar, L., Mutanga, O., 2018. Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sensing 10, 1509. https://doi.org/10.3390/rs10101509
• Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
• Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2020.112002

• Donchyts, G., Baart, F., Winsemius, H., Gorelick, N., Kwadijk, J., van de Giesen, N., 2016. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 6, 810. https://doi.org/10.1038/nclimate3111
• Pekel, J.-F., Cottam, A., Gorelick, N., Belward, A.S., 2016. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422. https://doi.org/10.1038/nature20584
• Wu, Q., Lane, C.R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H.E., Lang, M.W., 2019. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sens. Environ. 228, 1–13. https://doi.org/10.1016/j.rse.2019.04.015
• Yamazaki, D., Trigg, M.A., 2016. Hydrology: The dynamics of Earth’s surface water. Nature. https://doi.org/10.1038/nature21100

• Li, X., Zhou, Y., Zhu, Z., Cao, W., 2020. A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth System Science Data 12, 357. https://doi.org/10.5194/essd-12-357-2020
• Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., Wang, S., 2018. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sens. Environ. 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055
• Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A.D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S., Wu, Q., Huang, K., Estes, L., Zeng, Z., 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability 1–7. https://doi.org/10.1038/s41893-020-0521-x
• Patel, N.N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F.R., Tatem, A.J., Trianni, G., 2015. Multitemporal settlement and population mapping from Landsat using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 35, 199–208. https://doi.org/10.1016/j.jag.2014.09.005
• Weiss, D.J., Nelson, A., Gibson, H.S., Temperley, W., Peedell, S., Lieber, A., Hancher, M., Poyart, E., Belchior, S., Fullman, N., Mappin, B., Dalrymple, U., Rozier, J., Lucas, T.C.D., Howes, R.E., Tusting, L.S., Kang, S.Y., Cameron, E., Bisanzio, D., Battle, K.E., Bhatt, S., Gething, P.W., 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336. https://doi.org/10.1038/nature25181

• Li, X., Zhou, Y., Meng, L., Asrar, G.R., Lu, C., Wu, Q., 2019. A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States. Earth System Science Data. 11(2), 881-894. https://doi.org/10.5194/essd-11-881-2019
• Robinson, N.P., Allred, B.W., Jones, M.O., Moreno, A., Kimball, J.S., Naugle, D.E., Erickson, T.A., Richardson, A.D., 2017. A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sensing 9, 863. https://doi.org/10.3390/rs9080863
• Xie, Z., Phinn, S.R., Game, E.T., Pannell, D.J., Hobbs, R.J., Briggs, P.R., McDonald-Madden, E., 2019. Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation. Remote Sens. Environ. 232, 111317. https://doi.org/10.1016/j.rse.2019.111317

• Dong, J., Xiao, X., Menarguez, M.A., Zhang, G., Qin, Y., Thau, D., Biradar, C., Moore, B., 3rd, 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 185, 142–154. https://doi.org/10.1016/j.rse.2016.02.016
• Xiong, J., Thenkabail, P.S., Gumma, M.K., Teluguntla, P., Poehnelt, J., Congalton, R.G., Yadav, K., Thau, D., 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sens. 126, 225–244. https://doi.org/10.1016/j.isprsjprs.2017.01.019
• Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., Gorelick, N., 2017. Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sensing 9, 1065. https://doi.org/10.3390/rs9101065

• Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., Hopkinson, C., 2019. Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote Sensing 11, 842. https://doi.org/10.3390/rs11070842
• Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z., Sun, R., Lan, G., Xie, G., Clinton, N., Giri, C., 2017. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sens. 131, 104–120. https://doi.org/10.1016/j.isprsjprs.2017.07.011
• Hird, J.N., DeLancey, E.R., McDermid, G.J., Kariyeva, J., 2017. Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sensing 9, 1315. https://doi.org/10.3390/rs9121315
• Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., Gill, E., 2018. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sensing 11, 43. https://doi.org/10.3390/rs11010043
• Wang, X., Xiao, X., Zou, Z., Chen, B., Ma, J., Dong, J., Doughty, R.B., Zhong, Q., Qin, Y., Dai, S., Li, X., Zhao, B., Li, B., 2020. Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine. Remote Sens. Environ. 238, 110987. https://doi.org/10.1016/j.rse.2018.11.030
• Wu, Q., Lane, C.R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H.E., Lang, M.W., 2019. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sens. Environ. 228, 1–13. https://doi.org/10.1016/j.rse.2019.04.015

• Carrasco, L., O’Neil, A.W., Morton, R.D., Rowland, C.S., 2019. Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sensing 11, 288. https://doi.org/10.3390/rs11030288
• Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693
• Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., Zhu, Z., 2017. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 202, 166–176. https://doi.org/10.1016/j.rse.2017.02.021
• Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., Liang, S., 2020. Annual Dynamics of Global Land Cover and its Long-term Changes from 1982 to 2015. Earth Syst. Sci. Data. 12, 1217–1243. https://doi.org/10.5194/essd-12-1217-2020

• DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J.W., Lang, M.W., 2020. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sens. Environ. 240, 111664. https://doi.org/10.1016/j.rse.2020.111664
• Liu, C.-C., Shieh, M.-C., Ke, M.-S., Wang, K.-H., 2018. Flood Prevention and Emergency Response System Powered by Google Earth Engine. Remote Sensing 10, 1283. https://doi.org/10.3390/rs10081283

• Vos, K., Splinter, K.D., Harley, M.D., Simmons, J.A., Turner, I.L., 2019. CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery Environmental Modelling and Software. 122, 104528. https://doi.org/10.1016/j.envsoft.2019.104528

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To the extent possible under law, Qiusheng Wu has waived all copyright and related or neighboring rights to this work.