Snow recognition in mountain areas based on SAR and optical remote sensing data

Snow cover in cold and arid regions is a key factor controlling regional energy balances, the hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, SAR technology offers the ability to measure snow wetness changes in all weather. In the present study, a new approach using combined SAR and optical data has been developed for dry and wet snow cover recognition in mountain areas. In this method, RadarSat-2 interferometric coherence images and backscattering coefficient images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth”, a dynamic thresholding algorithm was used to identify snow cover using interferometric coherence and local incidence angle images, and polarimetric target decomposition method was used to classify dry and wet snow cover. The classification results demonstrate that dry and wet snow cover extraction using this method can achieve 93.5% in snow-melt period.