Teluguntla, P and Thenkabail, P S and Oliphant, A and Aneece, I and Biggs, T and Gumma, M K and Foley, D and McCormick, R and Neelam, R and Long, E and Lawton, J (2025) Landsat-Derived Rainfed and Irrigated-Area Product for Conterminous United States for the Year 2020 (LRIP30 CONUS 2020) Using Supervised and Unsupervised Machine Learning on the Cloud. Photogrammetric Engineering & Remote Sensing, 91 (11). pp. 1-12. ISSN 0099-1112
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Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed- and irrigated-area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel-based supervised random forest classifier, and pixel-based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer’s accuracies of 90.2% and 95.7%, respectively, and user’s accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (69.3%) was classified as rainfed and 44.5 Mha (30.7%) was classified as irrigated. State-level summaries highlight regional differences and their implications for national and global food and water security.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Resilient Farm and Food Systems |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Landsat, Machine Learning, Remote Sensing, irrigated cropland, multispectral, rainfed cropland, spectral matching techniques, vegetation |
| Subjects: | Others > Rainfed Agriculture Others > GIS Techniques/Remote Sensing Others > Irrigation |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 21 Oct 2025 03:32 |
| Last Modified: | 21 Oct 2025 03:32 |
| URI: | http://oar.icrisat.org/id/eprint/13371 |
| Official URL: | https://www.ingentaconnect.com/content/asprs/pers/... |
| Projects: | UNSPECIFIED |
| Funders: | UNSPECIFIED |
| Acknowledgement: | This study was funded by National Aeronautics and Space Administration (NASA) Grant NNH13AV82I through its MEaSUREs (Making Earth System Data Records for Use in Research Environments) initiative. The U.S. Geological Survey (USGS) provided supplemental funding from other direct and indirect means through the Climate and Land Use Change Mission Area, including the Land Change Science and Land Remote Sensing programs. The project was led by the USGS in collaboration with the International Crops Research Institute for the Semi-Arid Tropics. The authors thank Dr. Aparna Phalke for reviewing initial versions of the document and Dr. Jun Xiong for providing the USGS internal review. Also, the authors thank two anonymous journal peer reviewers for providing constructive comments. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. |
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