Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals

Satellite microwave radiometer measurements have been used to extract snow depth information for approximately three to four decades. However, the coarse spatial resolution of satellite microwave radiobrightness observations affects the snow depth retrieval accuracy and hinders the application of snow depth data in studies on water, energy, and carbon cycles. Therefore, snow depth measurements require better spatial resolution to improve the retrieval accuracy and enhance the resolution of the data. In this paper, an improved linear unmixing method is proposed to improve the accuracy of existing methods and downscale brightness temperatures from satellite observations to improve snow depth estimates. Tests conducted on a dataset consisting of both simulated and satellite data show the effectiveness of the proposed method. The unmixing method is then applied to FengYun-3B satellite/microwave radiation imager measurements for snow depth retrievals, and the resulting snow depths are compared with weather station observations from January to February 2011. The results show that the snow depth estimated from downscaled brightness temperatures performs better than the original data and presents higher correlations and lower root mean square errors.