**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS RESEARCH ADDRESSES THE CRITICAL NEED TO ENHANCE THE ACCURACY AND UTILITY OF NATIONAL SOIL MOISTURE (SM) AND EVAPOTRANSPIRATION (ET) PRODUCTS BY INTEGRATING NEW DATA SOURCES AND DOWNSCALING THEM TO FARM-SCALE. THE GOAL OF THIS PROJECT IS TO DEVELOP NATIONAL HIGH-RESOLUTION SM AND ET PRODUCTS BY USING MACHINE-LEARNING APPROACHES TO INTEGRATE SATELLITE, IN SITU AND MODEL- DERIVED DATA, DOWNSCALE THEM TO FIELD SCALE AND DISSEMINATE THEM IN NEAR-REAL-TIME. THIS PROJECT SPECIFICALLY ADDRESSES THE FACT PRIORITIES BY INTEGRATING DISPARATE DATASETS AND BY BUILDING A SCALABLE DATA INFRASTRUCTURE SYSTEM FOR COLLECTING, PROCESSING AND DISTRIBUTING SM AND ET DATA TO AGRICULTURAL PRODUCERS, AGRIBUSINESSES, NATURAL RESOURCE MANAGERS AND SCIENTISTS. THESE DATA ARE IMPORTANT FOR SUPPORTING ON-FARM DECISION MAKING FOR APPLICATIONS SUCH AS PRECISION AGRICULTURE AND IRRIGATION SCHEDULING. THEY ALSO ARE IMPORTANT FOR MODELING CROP YIELD, AS WELL AS INSECT AND DISEASE OUTBREAKS. THE RESULTS OF THIS PROJECT WILL CREATE SUBSTANTIAL VALUE FOR THE U.S. AGRICULTURAL ENTERPRISE.