Seasonal snow cover classification based on SAR imagery and topographic data

Abstract

ABSTRACT Melting snow is a sensitive indicator of regional climate change and snowmelt runoff. Optical remote sensing offers an effective tool to delineate the extent of snow cover, but its application is limited by sky conditions and illumination. In contrast, synthetic aperture radar (SAR) has the advantages of high penetration and can obtain the bulk properties of snowpack, such as snow wetness, in addition to the snow-covered area (SCA). The widely used algorithm based on the multitemporal backscattering coefficient can delineate wet snow and further estimate dry snow cover by empirical rules. However, employing a fixed threshold on wet snow extraction and only considering altitude in dry snow estimation lead to the misclassification of snow cover. In this study, an algorithm was developed for dry and wet snow discrimination based on C-band dual-polarization SAR data. First, the adaptive threshold obtained by the Otsu thresholding method (OTSU) was implemented on the ratio image to improve wet snow extraction, and then topographic information, including elevation, slope and aspect, was added to further infer dry snow cover. Snow field observations were carried out in March 2021 and February 2017 to evaluate the effectiveness of the presented method in typical snow regions with different snowpack characteristics. Compared with using a single equation with a fixed threshold (57.9% and 56.5%, respectively), the overall accuracy of the novel approach in prairie and alpine snow areas was 84.2% and 73.9%, respectively. The results show that considering the adaptive threshold and topography offers superior performance in identifying dry and wet snow.