EVAPOTRANSPIRATION (ET) IS A KEY PROCESS IN THE CYCLE OF WATER AND ENERGY BETWEEN THE EARTH'S SURFACE AND THE ATMOSPHERE. IT CONSTITUTES ONE OF THE MAIN COMPONENTS OF THE TERRESTRIAL WATER CYCLE, AND ITS QUANTIFICATION IS A KEY TOOL FOR DETERMINING WATER AVAILABILITY. ACCURATELY MEASURING/MODELING ET IS CRITICALLY IMPORTANT FOR DAILY WATER RESOURCE MANAGEMENT, PARTICULARLY IN THE AREAS OF SUSTAINABLE AGRICULTURE. HOWEVER, GENERATING SUCH ET ESTIMATES THAT INFORM DAILY DECISION-MAKING AT FARMS IS CHALLENGING DUE TO SPATIAL VARIABILITY IN PROCESS- BASED ET MODEL PERFORMANCE AND THE LACK OF HIGH PRECISION OBSERVATIONS. THIS RESEARCH HARNESSES PUBLICLY AVAILABLE REMOTE SENSING DATA, PROCESS-BASED ET MODELS DEVELOPED BY THE SCIENTIFIC COMMUNITY, AND MACHINE-LEARNING ALGORITHMS TO GENERATE ACCURATE, HIGH-RESOLUTION ET ESTIMATES. THIS EFFORT INTEGRATES MULTI-SCALE SATELLITE OBSERVATIONS AND OTHER EARTH OBSERVATIONS TO ENHANCE THE TEMPORAL AND SPATIAL RESOLUTIONS OF OBSERVATIONAL DATA USING DEEP LEARNING. OUR PROJECT, FINEET, COMBINES PROCESS-BASED MODELS AND EMPIRICAL MODELS TO GENERATE A HETEROGENEOUS ENSEMBLE THAT IS CUSTOMIZED TO THE PARTICULAR SPATIAL REGION TO PRODUCE ACCURATE ET ESTIMATES. FINEET ALSO ENABLES EFFECTIVE CONUS-SCALE VISUALIZATIONS OF ET ESTIMATES ALLOWING USERS TO EXPLORE CONTRIBUTING FEATURES (OR INDEPENDENT VARIABLES) AND ESTIMATES FROM DIVERSE MODELS. THE PROPOSED DATA INTEGRATION, ANALYTICS, AND FORECASTING INFRASTRUCTURE WILL ENABLE END-TO-END ANALYSIS OF FARM-LEVEL ET DATA FROM DIAGNOSTIC TO PREDICTIVE ANALYTICS. OUR LONG-TERM GOAL IS TO ENHANCE THIS FRAMEWORK TO SUPPORT OTHER REMOTE SENSING PLATFORM WHILE ALSO ACCOUNTING FOR DIVERSE ENVIRONMENTAL/ECOLOGICAL PARAMETERS THAT WILL LEAD TO EFFECTIVE DECISION-MAKING AND EXTEND THE MODEL TO GLOBAL SCALES. WE COMBINE EXPERTISE IN COMPUTER SCIENCE, AGRICULTURE, ECOLOGY, AND STATISTICS TO CREATE CYBERINFRASTRUCTURE TOOLS THAT DEMOCRATIZE ACCESS TO CUTTING-EDGE DEVELOPMENTS IN MACHINE LEARNING AND BIG DATA TO STAKEHOLDERS IN THE AGRICULTURAL COMMUNITY.