The knowledge of hydrological variables (e.g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological
models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e.g. soil moisture) or averaged over large regions (e.g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS.
Retrieval of hydrological variables (e.g. soil moisture)from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies.