Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models

Abstract

Snow depth (SD) is essential for studying climate change and hydrological cycle on the Tibetan Plateau (TP). Despite the effectiveness of passive microwave remote sensing for large-scale SD measurement, its low spatial resolution and scanning gaps limit its application, particularly in the TP region where the terrain is complex and snow distribution exhibits obvious heterogeneity. This study developed Advanced Microwave Scanning Radiometer 2 (AMSR2) SD downscaling models for the TP using ensemble learning methods and AMSR2 brightness temperature data from October 1, 2012, to April 30, 2021. We employed five ensemble methods—AdaBoost, GBDT, XGBoost, LightGBM, and Random Forest—with LightGBM achieving the highest accuracy (RMSE=2.66 cm). Recursive feature elimination (RFE) was applied to the LightGBM model, optimizing factor selection and maintaining high accuracy. The models excelled in estimating shallow snow areas (SD<5 cm) with an RMSE of 1.60 cm. SHapley Additive exPlanations (SHAP) values were used to quantify global and local contributions of each factor in the modeling process. Key factors included snow cover days, meteorological influences, and brightness temperature (BT) at 89 GHz with horizontal polarization, although their contributions varied significantly across the TP due to environmental gradients. The resulting 500 m SD estimates offer detailed and accurate snow distribution information in complex mountainous regions. Our results help to improve water resource management and climate change analysis on the TP.

Keywords

• Snow depth
• Downscaling
• Machine learning
• Tibetan Plateau
• AMSR2