** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** NATIONAL AGRICULTURAL STATISTICS SERVICE (NASS) CONDUCTS WEEKLY SURVEYS OF CROP AND SOIL MOISTURECONDITIONS FOR U.S. CROPLAND AND PROVIDES COARSE-RESOLUTION SATELLITE SOIL MOISTURE ANDVEGETATION CONDITIONS VIA A WEB APPLICATION CROP-CASMA. HOWEVER, ITS COARSE-RESOLUTIONMAPS ARE UNABLE TO CAPTURE FIELD/SUBFIELD LEVEL SOIL MOISTURE VARIATIONS. IT IS URGENTLY NEEDED TODEVELOP FIELD/SUBFIELD-LEVEL SOIL MOISTURE MAPS FOR NASS AND THE AGRICULTURAL COMMUNITY TO MONITORCROP GROWTH CONDITIONS AND ASSESS DROUGHT OR FLOOD IMPACT. THIS PROJECT WILL ESTABLISH A PARTNERSHIPBETWEEN US AND CANADIAN INSTITUTES TO DEVELOP NEW MACHINE LEARNING HIGH-RESOLUTION SOILMOISTURE (ML-HRSM2.0) PRODUCTS IN SUPPORT OF NASS CROP MONITORING AND ASSESSMENT. IN SITUNETWORKS, SATELLITE IMAGERY AND MODEL-DERIVED WEATHER, SOIL MOISTURE, TERRAIN, AND SOIL MAPS WILL BECOMBINED TO PREDICT DAILY SOIL WATER CONTENT (SWC) AND PLANT AVAILABLE WATER STORAGE (PAWS) AT 100-M AT THE SURFACE AND ROOTZONE SINCE 2016. WE WILL COMBINE ML MODELS WITH A PROCESS MODEL VIA DATAASSIMILATION TO DEVELOP CROP SOIL MOISTURE CONDITION MAPSAND NASS WEEKLY SOIL MOISTURE CONDITION REPORTS AND DISSEMINATE ALLMAPS OVER CROP-CASMA FOR ENHANCING NASS SOIL MOISTURE CONDITION MONITORING OPERATION ANDFOR FREE PUBLIC USE. IT IS EXPECTED THAT USING ML-HRSM2.0 PRODUCT WILL HELP NASS AND THE AGRICULTURAL COMMUNITY IMPROVE CROP CONDITION MONITORING, DISASTER ASSESSMENT, ANDOPERATIONAL DECISION MAKINGS.