$\times6.25$ 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 6.32 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.">

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

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