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A Deep Learning Approach to Mapping Irrigation Using Landsat: IrrMapper U-Net., , , and . IEEE Trans. Geosci. Remote. Sens., (2022)A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net., , , and . CoRR, (2021)IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S., , , , , and . Remote. Sens., 12 (14): 2328 (2020)Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine., , , , , , , , , and 3 other author(s). IGARSS, page 2320-2323. IEEE, (2020)DroughtCast: A Machine Learning Forecast of the United States Drought Monitor., , , , and . Frontiers Big Data, (2021)Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data., , , , , , , , and . IGARSS, page 309-312. IEEE, (2021)Smap L4 Assessment of the Us Northern Plains 2017 Flash Drought., , , , , and . IGARSS, page 5366-5369. IEEE, (2019)Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data., , , , , , and . Remote. Sens., 10 (3): 372 (2018)Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product., , , , and . Frontiers Big Data, (2020)A satellite-driven hydro-economic model to support agricultural water resources management., , , , , , , , and . Environ. Model. Softw., (2020)