My research focuses on remote sensing of hydrologic water cycle. The overarching goal is to better understand changes in key water and energy fluxes at the land-atmosphere interface for improving global water and food security in a changing climate. The objective is to develop modern algorithms that invert forward radiative transfer models that relate key hydrologic state variables and fluxes (e.g., precipitation and soil moisture) to multi-frequency satellite observations from visible to microwave bands.
Another area of my research is focused on optimal estimation of initial conditions of prognostic equations of land-atmosphere dynamics through sparsity promoting data assimilation. The goal is to integrate remote sensing big-data with the knowledge of environmental models to extend their skills in prediction of environmental extremes.