Abstract
In this work the possibilities of combining modelled (GEOtop, Hydrological model) and remotely sensed (ENVISAT ASAR WS) soil moisture content (SMC) values were investigated introducing a novel approach for data fusion on a product level. Data fusion was performed through the definition of a correction term for the modelled SMC dataset. For the determination of this term machine learning (Support Vector Regression) was used. As a reference dataset in-situ SMC measurements were considered. The benefit of the proposed method was successfully shown as R-2 between modelled and measured SMC values was improved from 0.11 to 0.61.