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Article

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

1
Montana Climate Office, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
2
Department of Geosciences, University of Montana, Missoula, MT 59812, USA
3
Department of Ecosystem and Conservation Sciences, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
4
School of Natural Sciences, California State University Monterey Bay, Seaside, CA 93955, USA
5
Cooperative for Research in Earth Science and Technology, NASA Ames Research Center, Moffett Field, CA 94035, USA
6
Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA
7
Division of Hydrologic Sciences, Western Regional Climate Center, Desert Research Institute, Reno, NV 89512, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2328; https://doi.org/10.3390/rs12142328
Received: 15 May 2020 / Revised: 9 July 2020 / Accepted: 14 July 2020 / Published: 20 July 2020
High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California. View Full-Text
Keywords: Irrigation; Landsat satellite; random forest Irrigation; Landsat satellite; random forest
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MDPI and ACS Style

Ketchum, D.; Jencso, K.; Maneta, M.P.; Melton, F.; Jones, M.O.; Huntington, J. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sens. 2020, 12, 2328. https://doi.org/10.3390/rs12142328

AMA Style

Ketchum D, Jencso K, Maneta MP, Melton F, Jones MO, Huntington J. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing. 2020; 12(14):2328. https://doi.org/10.3390/rs12142328

Chicago/Turabian Style

Ketchum, David; Jencso, Kelsey; Maneta, Marco P.; Melton, Forrest; Jones, Matthew O.; Huntington, Justin. 2020. "IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S." Remote Sens. 12, no. 14: 2328. https://doi.org/10.3390/rs12142328

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