Improvement of TOPLATS-based discharge predictions through assimilation of ERS-based remotely sensed soil moisture values

In this paper, we investigate the possibility to improve discharge predictions from a lumped hydrological model through assimilation of remotely sensed soil moisture values. Therefore, an algorithm to estimate surface soil moisture values through active microwave remote sensing is developed, bypassing the need to collect in situ ground parameters. The algorithm to estimate soil moisture by use of radar data combines a physically based and an empirical backscatter model. This method estimates effective soil roughness parameters, and good estimates of surface soil moisture are provided for bare soils. These remotely sensed soil moisture values over bare soils are then assimilated into a hydrological model using the statistical correction method. The results suggest that it is possible to determine soil moisture values over bare soils from remote sensing observations without the need to collect ground truth data, and that there is potential to improve model-based discharge predictions through assimilation of these remotely sensed soil moisture values.