Snow depth retrieval from microwave remote sensing by combining wavelet transform and machine learning models in Northern Xinjiang, China

Passive microwave remote sensing is a valuable tool for snow depth estimation. However, accurate retrieval is limited by nonlinear relationships between the snow depth and passive microwave brightness temperature (TB) that are caused by snow physical properties, underlying surface type, and topographical factors. Our study aims to enhance snow depth estimation in Northern Xinjiang (NX), China, utilizing Advanced Microwave Scanning Radiometer 2 TB data (with a resolution of 0.1 deg) and fractional snow cover products through a combination of wavelet transform and two artificial neural network (ANN) models: feedforward neural network (FFNN) and generalized regression neural network (GRNN). The hybrid models were trained and validated using in situ snow depth observations from 44 stations across NX. Results indicate that applying wavelet transform reduces the root-mean-square error (RMSE) by 28.88% for FFNN. In the snow season of 2013 to 2014, Wavelet-GRNN (RMSE: 7.36 cm, NSE: 0.59, R: 0.78, bias: 1.68 cm) outperforms Wavelet-FFNN (RMSE: 8.26 cm, NSE: 0.48, R: 0.75, bias: 1.69 cm) by 10.90%. However, Wavelet-FFNN exhibits superior performance, up to 13.78% than Wavelet-GRNN in complex topographic areas like Xiaoquzi station. In addition, spatial–temporal estimations demonstrate that the hybrid models surpass three well-known snow depth products and alleviate issues of excessively high or low values in NX. These findings underscore the effectiveness of hybrid models combining wavelet transform and ANNs, integrating passive microwave remote sensing and auxiliary data, for accurate snow depth estimation in mountainous regions.