Estimation of Snow Depth from Multi-source Data Fusion Based on Data Assimilation Algorithm

In snow depth studies,the smoothing effect of ground data interpolation and the low spatial resolution of remote sensing have a great impact on the estimation accuracy. In this paper,by using cloud-removed snow cover products from the fusion of MODIS (moderate resolution imaging spectro-radiometer) and AMSR-E (advanced microwave scanning radiometer - EOS) to construct virtual station,we make up the shortage of meteorological stations less and unevenness and correct the smoothing effect of Kriging interpolation. At the same time,a new scheme is proposed to improve the estimation accuracy of snow depth interpolation,which integrates data assimilation algorithm and Kriging method to fuse ground-based snow depth measurements,MODIS snow cover products. and snow depth derived from the AMSR-E microwave brightness temperature. This method was applied in the area of northern Xinjiang. Three independent stations at different elevations were chosen to evaluate fusion results. The results indicate that the proposed algorithm can effectively improve the accuracy of snow depth spatial distribution.