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Remote sensing in terrestrial hydrology; developing, calibrating, & validating remote sensing and data assimilation algorithms through extensive interdisciplinary field experiments; modeling remote sensing signatures of dynamic vegetation at various spatio-temporal scales, modeling water and energy fluxes at land surface; and assimilating active & passive(More)
In this study, a novel machine learning algorithm is proposed to disaggregate coarse-scale remotely sensed observations to finer scales, using correlated auxiliary data at the fine scale. It includes a regularized Cauchy-Schwarz distance based clustering step that assigns soft memberships to each pixel at the fine-scale followed by a kernel regression that(More)
—Microwave backscatter from vegetated surfaces is influenced by vegetation structure and vegetation water content (VWC), which varies with meteorological conditions and moisture in the root zone. Radar backscatter observations are used for many vegetation and soil moisture monitoring applications under the assumption that VWC is constant on short(More)