Jasmeet Judge

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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)
An novel algorithm is proposed to downscale microwave brightness temperatures (T B), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to T B(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)