Assimilation of SVM-based estimates of land surface temperature for the retrieval of surface energy balance components

Data-assimilation methods play a crucial role for exploiting remote sensing in dynamic physical models for the prediction of hydrological-process evolution. Here, a novel method is proposed to assimilate land-surface temperature estimates, derived by applying support-vector regression to infrared satellite data, into a variational technique for mass and energy exchange estimation at the soil surface. Recent techniques to fully automate support vector regression and to estimate the pixelwise statistics of the regression error are incorporated in the proposed method.