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Abstract: A machine learning approach to improve the spatiotemporal resolution of actual evapotranspiration estimates by upscaling eddy covariance tower measurements to the field scale (AGU25)

A machine learning approach to improve the spatiotemporal resolution of actual evapotranspiration estimates by upscaling eddy covariance tower measurements to the field scale

Ali Dadkhah1, Donna M Rizzo2, Paul R Adler3, Arman Ahmadi4, Noah B. Beckage5, Patrick J Clemins6, Luis A Garcia1, Matthew W Myers3, George F Pinder1, Shaurya Swami7 and Asim Zia8, (1)University of Vermont, Department of Civil and Environmental Engineering, Burlington, VT, United States, (2)University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, (3)USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, United States, (4)University of California, Davis, Biological and Agricultural Engineering, Davis, United States, (5)University of Vermont, Burlington, United States, (6)University of Vermont, Department of Computer Science, Burlington, VT, United States, (7)University of Vermont, Department of Computer Science, Burlington, United States, (8)University of Vermont, Department of Community Development and Applied Economics, Department of Computer Science, Burlington, VT, United States
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