LAUSR

Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis-support vector machine (PCA-SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer (MODIS) snow cover products and the Sentinel-1 synthetic aperture radar (SAR) scattering characteristics. First, derived from the Sentinel-1A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis (PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation (FB 1 =93.86, FB 2 =59.78). The evaluation of the threat score (TS), probability of detection (POD), and false alarm ratio (FAR) for the snow-covered pixels obtained from the two-stage SAR images were different (TS 1 =86.84, POD 1 =90.10, FAR 1 =4.01; TS 2 =56.40, POD 2 =57.62, FAR 2 =3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.