Abstract
The main idea of this study is the development of an innovative data fusion method through which state-of-the-art remotely sensed products and hydrological modelling simulations can be integrated to improve the retrieval and the reliability of snow cover and snow water equivalent mapping. The proposed method is based on a machine learning technique, Support Vector Machine (SVM), and on exploitation of two well-instrumented test-sites in EUREGIO region for the validation. Results show an improvement of performances with respect to single products from remote sensing and model. On EUREGIO scale the accuracy of snow cover mapping obtained from fusion reaches 0.95.