Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention.
In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. In particular, the SAR images have demonstrated to be effective and robust measures to identify wet snow.
The wet snow maps are derived with a novel multi-temporal approach that exploits the high temporal resolution provided by the Sentinel-1 mission. In detail, the method identifies the wet snow on the ground by considering both the current value of the coefficient of backscattering and its temporal evolution. The proposed approach is designed to work in alpine environments.