Wet snow detection using dual-polarized Sentinel-1 SAR time series data considering different land categories

Snowmelt is a natural water resource, and its distribution is essential for understanding regional climate change and hydrological cycle. In this study, using dual-polarized C-band Sentinel-1 SAR data, we propose a new wet snow detection algorithm that considers the effects of radar incidence angle, land cover types and topography for snow regions where prairie snow is the main snow cover type. We quantitatively evaluate the performance of the proposed method by optical-derived snow maps. The results show that (1) the overall accuracy of the proposed method is 80.8% (dry snow reference) and 79.0% (snow-free reference); while using a conventional threshold of -2 dB, the accuracy is 32.1% and 43.5%, respectively; (2) -2 dB can only be applied on the ratio image calculated by the snow-free reference to extract wet snow in cropland; (3) adding topographic information greatly improves the identification accuracy, especially for grassland and barren with higher topographic complexity.