Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR and Optical Remote Sensing Data

Snow cover in mountain areas is a key factor controlling regional energy balances, 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, synthetic aperture radar (SAR) offers the ability to measure snow wetness changes in all weather. In this study, a novel method, which can be approached in two steps by using SAR and optical data, has been developed for dry and wet snow cover recognition in mountain areas. First, two ground-based synchronous observations were implemented, respectively, for snow-accumulation period and snow-melt period. Then, the RADARSAT-2 interferometric coherence images and the backscattering coefficient images of the two periods are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the "ground truth." A dynamic thresholding algorithm was proposed to identify snow cover by taking the polarization mode, local incidence angle, and underlying surface type into consideration. Finally, 36 polarimetric parameters obtained from Pauli, H/A/alpha, Freeman, and Yamaguchi decomposition were analyzed; the results indicate that P-vol from Pauli, lambda(3) from H/A/alpha, and Y-vol from Yamaguchi are more applicable to discriminate dry and wet snow. These three factors, combined with training samples from Nagler algorithm and in situ data, were used to build a support vector machine to classify the extracted snow cover to dry and wet snow. The classification results demonstrate that the dry and wet snow cover extraction can achieve an accuracy of 90.3% compared with in situ measurements.