Snow Depth Downscaling Retrieval Based on Spatial-Environment XGBoost Model: A Case Study of the Arid Region of Northwest China

As a crucial component of the cryosphere, snow exhibits high sensitivity to climate change and exerts a significant influence on the hydrological cycle and ecological framework. Snow depth (SD) is one of the important information about snow, so studying high-resolution SD data is of great significance for regional climate, hydrology, and disasters. Utilizing passive microwave data from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and optical remote sensing data from SSE mod fractional snow cover (FSC), along with digital elevation model (DEM) and land cover type datasets, the extreme gradient boosting (XGBoost) model is employed to leverage the advantages of multisource remote sensing information. Subsequently, the spatial-environmental-XGBoost (SE-XGB) model is constructed for the downscaling retrieval of SD. In the arid region of Northwest China, the inversion results of SE-XGB model were validated against measured SD at the weather station. The validation yields an $R$ of 0.912, root mean square error (RMSE) of 2.65 cm, and mean absolute error (MAE) of 0.81 cm. The SD estimated by the SE-XGB algorithm exhibits excellent agreement with the measured data. This study validated the snow inversion accuracy of four major land cover types, showing that bare land had the highest accuracy. In contrast, the inversion accuracies for cultivated land, urban land, and grass land exhibit roughly comparable performance, yet all three demonstrate significantly lower accuracy than bare land. The validation regions selected for this study include northern Xinjiang and Northeast China. At the same time, the SE-XGB algorithm is verified in the northern Xinjiang Line 1 and line 2 of China snow survey. The SE-XGB algorithm exhibits enhanced accuracy in terms of RMSE and MAE, as well as significantly reduced production time, when compared to the down-scale SD inversion algorithm spatial dynamic downscaling (SDD) algorithm employed by similar products. The SE-XGB algorithm employed in this study exhibits remarkable performance in discriminating snow and achieves high precision in retrieving SD. This leads to a more detailed spatial distribution of snow information, effectively eliminating the large area block structure, and providing valuable References for the development of global downscaled SD datasets.