** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE ACCURATE ESTIMATION OF HIGH-RESOLUTION GEOSPATIAL CROP EVAPOTRANSPIRATION (ETC) IS CRUCIAL FOR PRECISE IRRIGATION MANAGEMENT AND DEVELOPMENT OF ETC PRESCRIPTION MAPS FOR VARIABLE-RATE IRRIGATION SYSTEMS. ONE FAVORED APPROACH INVOLVES UTILIZING UNMANNED AERIAL SYSTEMS (UAS)-BASED IMAGERY WITHIN THE METRIC ENERGY BALANCE MODEL TO CALCULATE DAILY ETC AT A RESOLUTION LESS THAN 10 CM/PIXEL. HOWEVER, PREVIOUS RESEARCH HAS REVEALED INACCURACIES IN ETC ESTIMATION, PRIMARILY DUE TO DIFFICULTIES IN IDENTIFYING HOT AND COLD PIXELS REPRESENTING DRY, BARE AGRICULTURAL SOIL AND FULLY IRRIGATED CANOPIES WITHIN UAS IMAGERY. THIS PROJECT AIMS TO IMPROVE HIGH-RESOLUTION ETC ESTIMATION USING UAS IMAGERY WITH EXPLORING ALTERNATIVE SOLUTIONS FOR THE HOT AND COLD ANCHOR PIXELS USED IN THE MODEL'S INTERNAL CALIBRATION AND DEVELOP MACHINE LEARNING (ML) MODELS BASED ON UAS-IMAGERY FOR ETC ESTIMATION. THE SPECIFIC OBJECTIVES INCLUDE: 1) ESTABLISHING EMPIRICAL EQUATIONS FOR TEMPERATURE ESTIMATION OF FULLY IRRIGATED CANOPIES AND DRY, BARE AGRICULTURAL SOIL BASED ON METEOROLOGICAL PARAMETERS. 2) DESIGNING AND CONSTRUCTING ARTIFICIAL HOT AND COLD REFERENCE SURFACES WITH CONTROLLED AUTOMATED TEMPERATURE FOR UAS DATA ACQUISITION. 3) EVALUATING THE APPLICATION OF REFERENCE SURFACES WITH AUTOMATED CONTROLLED TEMPERATURE FOR ETC ESTIMATION. 4) DEVELOPING UAS-IMAGERY ML MODELS FOR ETC ESTIMATION AND INVESTIGATING THEIR PERFORMANCE. FIELD EXPERIMENTS WILL BE CONDUCTED WITH WINTER CANOLA, COVER CROPS, GRASS, ALFALFA, AND CORN IN THE HOT AND HUMID CLIMATE OF TENNESSEE, AS WELL AS ALFALFA AND SOYBEAN IN HOT AND DRY CLIMATE OF TEXAS TO COLLECT ESSENTIAL DATA FOR THIS STUDY. THIS COLLABORATIVE PROJECT STRENGTHENS RESEARCH, EDUCATION, AND EXTENSION CAPABILITIES BY FOSTERING PARTNERSHIPS BETWEEN MINORITY-SERVING AND OTHER INSTITUTIONS.