LGRIP30 — Landsat-Derived Global Rainfed and Irrigated-Cropland Product (NASA LP DAAC)
Where crops get rain vs. a pipe — irrigated and rainfed cropland mapped at 30 m across CONUS, 2020 snapshot.
What this is
LGRIP30 (Landsat-Derived Global Rainfed and Irrigated-Cropland Product, 30 m, L3 V002) maps global cropland into three classes at 30 m resolution: non-cropland, irrigated cropland, and rainfed cropland. The nominal 2020 epoch is the current release. UFFDA serves the CONUS granules (17 10°×10° tiles) from a public mirror on GitHub Releases, proxied through Titiler with a categorical colormap showing only irrigated (blue) and rainfed (amber) pixels — non-cropland and water are transparent. The field-level Mode B arm returns the dominant irrigation class plus irrigated/rainfed % for any clicked field. License: NASA LP DAAC data policy applies. LGRIP30 V002 carries no additional license restriction; under NASA ESDIS open data policy, unmarked NASA Earth Science data defaults to CC0. Attribution to NASA, USGS, LP DAAC, and Teluguntla et al. (2024) is strongly encouraged. Cite: Teluguntla P., Thenkabail P., Oliphant A., Gumma M., Aneece I., Foley D., McCormick R., and Witzeman C. (2024). Landsat-Derived Global Rainfed and Irrigated-Cropland Product L3 2020 30 m V002 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/COMMUNITY/LGRIP/LGRIP30_L3.002
Why we surface it
Irrigation is one of the biggest signals in ag land use — it distinguishes weather-dependent fields from infrastructure-backed ones, and it shows up in water-use, productivity, and risk profiles. LGRIP30 is the cleanest open CONUS map of this at 30 m. The mirror is $0-cost and the COGs have 7 overview levels, so it renders well at national zoom without a custom tile pipeline.
What you can do with it
Shown for commercial use. Personal, research, and nonprofit use is often more permissive — switch the verdict lens on the catalog.
- Internal / individual use
- Public domain. Use it for anything — internal, resale, AI training, closed products. No attribution legally required (crediting the source is still the decent thing to do).
- Redistribute the data
- Public domain. Use it for anything — internal, resale, AI training, closed products. No attribution legally required (crediting the source is still the decent thing to do).
- Republish a derivative
- Public domain. Use it for anything — internal, resale, AI training, closed products. No attribution legally required (crediting the source is still the decent thing to do).
- Build into a private product
- Public domain. Use it for anything — internal, resale, AI training, closed products. No attribution legally required (crediting the source is still the decent thing to do).
This decoder is a starting point, not legal advice. For commercial deployment, run the actual license terms past someone who reads them for a living.
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