Remotely sensed observations from UAV-borne, airborne and satellite instruments have become an important tool to understand the hydrological and biophysical processes. The remotely sensed observations can be used directly for model parameterization or calibration, or indirectly to optimally update model states and fluxes via data assimilation. These techniques aim to improve the spatial representation of land surface processes and eventually our predicting capabilities. The goal of this session is to highlight recent research in remote sensing of hydrological and biophysical processes and states and to promote innovative methods to integrate them in conventional modeling frameworks. Areas of interest include; innovations in retrieval algorithms, novel blending of multi-sensor products, new applications of existing data to various processes and integration of remotely sensed observations into models via calibration and prediction updating schemes. Furthermore, studies that address land-atmosphere feedbacks and quantification of their results (e.g., drought, flood and heat waves) via remote sensing are particularly welcome.