Awesome-GEE

for an Earth Engine account

Read the Earth Engine API documentation -

Read another Earth Engine API documentation - . Make sure you have a good understanding of client-side objects vs server-side objects

Try out the or Python API (e.g., )

Read

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

Spectral-temporal segmentation algorithm

zecojls/tagee

Terrain Analysis in Google Earth Engine (TAGEE)

ee-palettes

A module for generating color palettes in Earth Engine to be applied to mapped data

A suite of tools designed for continuous land change monitoring in Google Earth Engine

A system for monitoring forest degradation and deforestation

Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm

spectral

Awesome Spectral Indices for the Google Earth Engine JavaScript API (Code Editor)

msslib

An Earth Engine JavaScript library for working with Landsat MSS image data

geeSharp

Pan-sharpening in the Earth Engine Code Editor

snazzy

Custom basemap styles in the Earth Engine Code Editor

ee-polyfill

Modern Javascript methods (ES6+) for the Earth Engine Code Editor

gee-blend

Various blending functions for Google Earth Engine

Collection of code goodies for Google Earth Engine (GEE)

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

RadGEEToolbox

Python package simplifying large-scale operations using Google Earth Engine (GEE) Python API for users who utilize Landsat (5, 8, & 9) and Sentinel 1 & 2 data

restee

A package that aims to make plugging Earth Engine computations into downstream Python processing easier

wxee

A Python interface between Earth Engine and xarray for processing weather and climate data

taskee

Monitor your Earth Engine tasks and get notifications on your phone or computer

geedim

Search, composite, and download Earth Engine imagery, without size limits

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

rgee

An R package for using Google Earth Engine

earthEngineGrabR

Simplify the acquisition of remote sensing data

A collection of 250+ examples for using Google Earth Engine with R

Earth Engine QGIS Plugin ( , ) - 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

Google

Philipp Gärtner

by Philipp Gärtner

by

by

Earth Engine Virtual Meetup on May 8, 2020

GeoInsider webinar on May 28, 2020

Machine learning and data fusion workshop on June 10, 2020

Getting Started with Earth Engine with Sabrina Szeto ( - )

Earth Engine Virtual Meetup on May 6, 2020 ( )

GeoInsider webinar - Cloud computing and interactive mapping with Earth Engine and open-source GIS ( - )

GeoInsider webinar 2 - Using Google Earth Engine for large-scale geospatial analysis: A case study of automated surface water mapping ( | )

on Research Gate

Aybar, C., Wu, Q., Bautista, L., Yali, R., & Barja, A. (2020). rgee: An R package for interacting with Google Earth Engine. . 5(51), 2272

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. . 202, 18–27

Wu, Q. (2020). geemap: A Python package for interactive mapping with Google Earth Engine. . 5(51), 2305

, Remote Sensing for Environmental and Societal Changes Using Google Earth Engine ( )

, Cloud Computing in Google Earth Engine for Remote Sensing ( )

, Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science ( , )

, Google Earth Engine Applications ( , )

, Remote Sensing of Land Change Science with Google Earth Engine ( , )

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.

Boothroyd, R., Williams, R., Hoey, T., Barrett, B., & Prasojo, O. (2020). Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change.

Kumar, L., Mutanga, O., 2018. Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. 10, 1509

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. 164, 152–170

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.

Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022). Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. , 14(14), 3253

Yang, L., Driscol, J., Sarigai, S., Wu, Q., Lippitt, C. D., & Morgan, M. (2022). Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing. , 22(6), 2416

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. . 6, 810

Pekel, J.-F., Cottam, A., Gorelick, N., Belward, A.S., 2016. High-resolution mapping of global surface water and its long-term changes. 540, 418–422

Radwin, M., & Bowen, B. (2024). Evolution of Great Salt Lake’s Exposed Lakebed (1984-2023): Variations in Sediment Composition, Water, and Vegetation from Landsat OLI and Sentinel MSI Satellite Reflectance Data. Geosites, 51, 1–23

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. . 228, 1–13

Yamazaki, D., Trigg, M.A., 2016. Hydrology: The dynamics of Earth’s surface water.

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. 12, 357

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. . 209, 227–239

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. 1–7

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. . 35, 199–208

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. 553, 333–336

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. . 11(2), 881-894

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. 9, 863

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. . 232, 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. . 185, 142–154

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. . 126, 225–244

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. 9, 1065

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. 11, 842

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. . 131, 104–120

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. 9, 1315

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. , 46(3), 360-375

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. 11, 43

Radwin, M., & Bowen, B. (2024). Evolution of Great Salt Lake’s Exposed Lakebed (1984-2023): Variations in Sediment Composition, Water, and Vegetation from Landsat OLI and Sentinel MSI Satellite Reflectance Data. Geosites, 51, 1–23

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. . 238, 110987

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. . 228, 1–13

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). , 12(22), 3758

Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., ... & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. , 9(1), 1-17

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. 11, 288

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. 342, 850–853

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. . 202, 166–176

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. . 12, 1217–1243

Radwin, M., & Bowen, B. (2024). Evolution of Great Salt Lake’s Exposed Lakebed (1984-2023): Variations in Sediment Composition, Water, and Vegetation from Landsat OLI and Sentinel MSI Satellite Reflectance Data. Geosites, 51, 1–23

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. . 240, 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. 10, 1283

Tellman, B., Sullivan, J.A., Kuhn, C., Kettner, A.J., Doyle, C.S., Brakenridge, G.R., Erickson, T.A., Slayback, D.A., 2021. Satellite imaging reveals increased proportion of population exposed to floods. 596, 80–86

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 . 122, 104528