Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach

Passive microwave (PM) remote sensing have been extensively used for snow depth (SD) estimation. However, current SD products from traditional PM data fail to capture the differentiation in mountainous and complex terrains with coarse resolution. Therefore, this study incorporates factors such as geographical location, topographic features, and land cover, along with various machine learning algorithms including Gaussian process regression (GPR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to construct and optimize SD estimation using enhanced-resolution PM data. The results demonstrate the following: (1)With the auxiliary variables, the SD product from LightGBM-based models exhibits the highest accuracy. (2)The performance of SD products from the LightGBM-based model varies monthly and annually, with shallow snow cover being slightly overestimated (30 cm). (3)The reliable SD product indicates spatial distribution characteristics in Xinjiang, with regions demonstrating no significant improvement being larger than those with no significant degradation. The above results illustrate the remarkable advantages of machine learning in capturing SD distribution and its spatio-temporal variation bolstered by enhanced PM data and auxiliary data.