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Awesome Earth Engine Awesome

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

Table of Contents

Earth Engine official websites

Get Started

  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.

Get Help

JavaScript API

Playground

Repositories

Tutorials

Python API

Installation

Packages

  • 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.
  • eemont - A Python package that extends the Google Earth Engine Python API with pre-processing and processing tools.
  • hydra-floods - An open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data.
  • restee - A package that aims to make plugging Earth Engine computations into downstream Python processing easier.

Repositories

Tutorials

R

Packages

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

Repositories

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

Tutorials

QGIS

Packages

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

Repositories

Tutorials

GitHub Developers

Community

Individuals

Twitter

Bots

Google affiliated

Individuals

Apps

Free Courses

Presentations

geemap

General

Videos

Google

General

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

geemap

Projects

Websites

Datasets

Community Datasets

Landsat

Sentinel

NAIP

Land Cover

Papers

Highlights

  • 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

Journal Special Issues

Review

  • 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
  • Boothroyd, R., Williams, R., Hoey, T., Barrett, B., & Prasojo, O. (2020). Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change. WIREs Water. https://doi.org/10.1002/wat2.1496
  • 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

Hydrology

  • 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

Urban

  • 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

Vegetation

  • 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

Agriculture

  • 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

Wetlands

  • 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., Brisco, B., Granger, J. E., Mohammadimanesh, F., Salehi, B., Banks, S., ... & Weng, Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine. Canadian Journal of Remote Sensing, 46(3), 360-375. https://doi.org/10.1080/07038992.2020.1802584
  • 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
  • Yancho, J. M. M., Jones, T. G., Gandhi, S. R., Ferster, C., Lin, A., & Glass, L. (2020). The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sensing, 12(22), 3758. https://doi.org/10.3390/rs12223758

Land Cover

  • 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

Disaster Management

  • 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

Coastal

  • 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

Contributing

Contributions welcome! Read the contribution guidelines first.

License

CC0

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