Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU - Wei Liu; Zihui Lin; Yuan Hu; Aodong Tian; Xintai Yuan; Jens Wickert (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Snow depth monitoring is crucial for hydrology, climate research, and avalanche prediction. While traditional global navigation satellite system (GNSS) reflectometer methods offer cost-effective snow thickness retrieval, they suffer from poor accuracy and robustness, especially in complex terrains and extreme weather. This study proposes an innovative snow depth retrieval technique employing a time-series recurrent neural network with bidirectional gated recurrent units (Bi-GRUs). Unlike traditional methods using signal-to-noise ratio (SNR) features, our algorithm utilizes the detrended SNR as Bi-GRU input, aiming to enhance accuracy, particularly in low snow depths and complex terrains. SNR observations from GPS L1 carriers at stations P351 and AB33 were analyzed. The Bi-GRU algorithm demonstrated high consistency with true snow depths at station P351 (coefficient of determination: 0.9766), with the root-mean-square error (RMSE) and the mean absolute error (MAE) of 9.1559 and 6.4185 cm, respectively. Compared to traditional methods, the Bi-GRU model improved the RMSE by 30.9% and the MAE by 44.5%. At station AB33, where snow depth variations were significant, accuracy improvements of 65.6% (RMSE: 7.4905 cm) and 63.2% (MAE: 5.6074 cm) were observed. In addition, the Bi-GRU model exhibited greater robustness compared to long short-term memory. These findings highlight the efficacy of the Bi-GRU-based approach, suggesting its superiority and broader applicability.