GLOBALLY GROUNDWATER WITHDRAWALS DRIVE CHANGES IN STORAGE. HOWEVER VERY FEW REGIONS OF THE U.S. AND WORLD MONITOR THEIR GROUNDWATER WITHDRAWALS AT THE LOCAL SCALE NECESSARY TO IMPLEMENT SUSTAINABLE MANAGEMENT SOLUTIONS. IN THIS PROPOSAL METHODOLOGIES WILL BE DEVELOPED THAT ESTIMATE BOTH UNCONFINED AND CONFINED GROUNDWATER WITHDRAWALS AT THE LOCAL SCALE (~1 KM). THIS WILL BE ACCOMPLISHED BY INTEGRATING DATA FROM ACTIVE AND PASSIVE SATELLITE SENSORS AT A WIDE RANGE OF SPATIAL AND TEMPORAL SCALES THAT ARE SENSITIVE TO DIFFERENT ASPECTS OF THE WATER BALANCE. A VARIETY OF DISPARATE SATELLITE SENSORS WILL BE USED INCLUDING LANDSAT SENTINEL-1 GRACE GRACE-FO MODIS TRMM GPM AND SMAP WHICH MEASURE DIFFERENT COMPONENTS OF THE WATER BALANCE AT RESOLUTIONS RANGING FROM 10 M TO 100S OF KM. WE PROPOSE TO INTEGRATE THESE INHERENTLY DIFFERENT REMOTE SENSING DATASETS USING A HYBRID WATER BALANCE/MACHINE LEARNING APPROACH WHICH CAN INTEGRATE DATASETS WITH A WIDE RANGE OF SPATIO-TEMPORAL RESOLUTIONS.