A Fine-Resolution Snow Depth Retrieval Algorithm From Enhanced-Resolution Passive Microwave Brightness Temperature Using Machine Learning in Northeast China

As one of the major components in the hydrological system, seasonal snow cover in Northeast China has drawn much attention recently. Because of the coarse spatial resolution of the passive microwave (PMW), heterogeneity of snowpack, and forest cover, it is difficult for existing snow products to achieve high precision snow parameters [e.g., snow depth (SD) or snow water equivalent (SWE)] assessment and hydrological research in fine scale. In this study, a novel SD retrieval algorithm that considered both the spatiotemporal dynamic of snow characteristics and forest attenuation was developed by combining the Calibrated Enhanced-Resolution Brightness Temperature (TB) data and other auxiliary information and produced a fine resolution (i.e., 6.25 km x 6.25 km) and high accuracy SD data in Northeast China. Instead of complex physical models, the machine learning was used to untangle the nonlinear complex relationship between SD and the enhanced-resolution TB, forest fraction (FF), and snow characteristics. The verification results at ground weather stations showed that the retrieved SD by the proposed algorithm had high consistency with the observed SD, its RMSE, bias, and correlation coefficient (R) of 632 cm, -0.23 cm, and 0.63, respectively. Compared with the existing SD products (WESTDC and AMSR2), the developed model greatly improved both spatial resolution and retrieval accuracy. In general, the fine-resolution SD inversion model achieved satisfactory accuracy and stability, and it will be used to generate a long-term SD dataset service for climate change and hydrological research in the future.