Exploring Radarsat-2 SAR Cross-Polarization Ratio Capability for Tundra Snow Depth Estimation Using Numerical and Deep Learning Approach

Abstract

Snow is essential to the Earth system, significantly influencing the global climate, freshwater availability, and economic activities. A significant snow cover decline has been reported in vast northern hemisphere areas. The decrease in snow cover will affect more than one-sixth of the world's population who rely on the snow as a freshwater supplement. Snow mass, often expressed as snow water equivalent (SWE), signifies the quantity of water held in the form of snow on the Earth's surface, and it plays a crucial role in the functioning of water, energy, and geochemical processes. Since the seasonal snow has a high spatial variability at regional and local scales, surface observations cannot provide sufficient SWE information. The quality of global SWE estimates needs to be improved. In recent years, C-band spaceborne SAR has shown a high potential for global monitoring of SWE. This thesis aims to explore the current spaceborne C-band SAR signal sensitivity to Arctic tundra snow and define a suitable approach for estimating deep snow depth through the tundra environment. The noticeable variation of C-band backscatter with the snow depth generally demonstrated the C-band sensitivity to dry snow for the deep snow. These areas are dominated by tall vegetation areas such as tall shrubs and riparian shrubs. The cross-polarization ratio method also shows a higher correlation with snow depth than cross-pol and co-pol backscatter, which is generally consistent with earlier research. Also, numerical, and deep learning (DL) approaches were tested based against drone-based snow depth reference data. The result shows that the numerical approach may only be valid in a place with over 2.5 m snowpack. Compared to the numerical approach, the DL approach shows a better evaluation, resulting in a correlation coefficient of R 0.79 between the estimated and target snow depth with a root mean square error (RMSE) of 19 cm. The DL approach can be more suitable for estimating snow depth from C-band SAR observation under the Arctic tundra snow environment.