Snow Covered Area Estimation for Forest Area in Northeastern China Based on Multi-temporal Sentinel-1 SAR Data

Abstract

The snow cover in the spring melting period is of great significance for hydrological and meteorological forecasting. Regional snowcover area (SCA) is the basic parameters of hydrological model. In recent years, Synthetic Aperture Radar (SAR) has been widely used in wet snow detection and SCA estimation during the spring melting period for its effectiveness. The absorption of electromagnetic waves by wet snow significantly reduces the SAR backscatter signal, which makes it possible to distinguish between wet snow and dry snow (or no snow). However, in forest areas, the sensitivity of traditional algorithms to detect snow changes is decreased due to the significant change of forest canopy transmittance with time and weather conditions. Based on the measured data of the forest area in northeastern China and the multi-temporal Sentinel-1 SAR data, the influence of forest canopy on the C-band backscattering coefficient of SCA is analyzed, and an adaptive SCA estimation algorithm based on the forest backscatter model is introduced in this paper. The backscattering coefficient attenuation caused by the forest canopy can be predicted by the algorithm and the SCA estimation for the corresponding region will be corrected. The results show that the snow cover characteristic is accurately estimated and the SCA of forests in northeastern China is significantly smaller than that of the open area, while the SCA of coniferous forest areas is generally smaller than broadleaf forests areas in the early stage of snowmelt period.