Estimation of wet snow cover area with SAR imagery in the basin of Tupungato River, Mendoza, Argentina

The data coming from remote sensing, in particular Synthetic Aperture Radars (SAR), have great potential in the study of snow since they provide information regardless of weather conditions. In this work, the capacity of the C band in wet snow detection is investigated, in the Tupungato river basin, province of Mendoza, Argentina. To this scope, Sentinel 1 images were used, processed following the methodology developed by Nagler and Rott (2000), which adopts a change detection technique by considering an image taken as a reference, in a snow-free or dry snow condition. This algorithm requires the adaptation of parameters according to the particular characteristics of the study area. The analysis carried out in this basin indicates that the optimal threshold to identify wet snow is -2dB. The results were validated indirectly by using information of surface temperature and snow cover area, obtained with LANDSAT 8 optical images. In this way, it was verified the correct classification with SAR of pixels corresponding to wet snow. Wet snow maps generated with SAR data are very useful to nourish hydrological models for the forecast of flow in mountain areas dominated by snow regime.