geemap
Python + Google Earth Engine without the pain — interactive mapping and analysis of petabyte-scale water data.
Planetary-scale geospatial analysis in the cloud — the data catalogue and compute behind modern water remote sensing.
by Google
Earth Engine pairs a multi-petabyte catalogue of satellite and environmental data — Landsat, Sentinel, MODIS, precipitation, terrain, land cover, the JRC Global Surface Water record — with cloud compute that runs your JavaScript or Python analysis next to the data. Water applications lead its success stories: global surface-water change, flood mapping, irrigation monitoring, snow and drought tracking.
It is free for noncommercial and research use, with commercial licensing through Google Cloud. The skill transfers everywhere in modern hydrology; start with the JavaScript Code Editor or geemap in Python.
The satellite and reanalysis datasets H&H engineers actually reach for — DEM to GRACE.
Ranked by the topics, categories and workflow stages they share with Google Earth Engine.
Python + Google Earth Engine without the pain — interactive mapping and analysis of petabyte-scale water data.
Pixel-level surface water history since 1984, built from the entire Landsat archive — where water has been, and when.
Near-real-time 10m land cover from Google/WRI, updated every Sentinel-2 pass — land cover that keeps up.
Free 10-20m multispectral imagery, 5-day revisit — the imagery backbone under WorldCover and Dynamic World.
A STAC API and Jupyter environment over petabytes of satellite data — Sentinel, Landsat, MODIS, ERA5, CHIRPS and more.
The unified search portal across every NASA Earth-observation archive — SRTM, MODIS, SMAP, GPM, GRACE and more.