An Improved Algorithm Based on Wavelet Decomposition to Retrieve Snow Depth Using GNSS-R Signals

  Objectives  Snow depth retrieval using global navigation satellite system (GNSS) reflected signals has the characteristics of low cost, low power consumption and all-day data collection. Because the signal-to-noise ratio (SNR) observations of GNSS are greatly affected by the noise power, we aim to retrieve snow depth in low noise and analyze snow depth variations in different snow surface characteristics.

  Methods  An improved algorithm is proposed to retrieve snow depth by introducing wavelet decomposition before spectral analysis, which can effectively separate the noise power from the signal power without changing the frequency composition of the original signal. This algorithm is verified by the SNR data collected at the Arctic Yellow River Station in the Winter and Spring of 2017. Furthermore, considering the complex variation of snow depth at the Yellow River Station, the applicability of this improved algorithm under different snow surface conditions was analyzed.

  Results  Compared with the original algorithm, the maximum bias of the improved algorithm between the estimation and the measurement decreases from 13.71 cm to 9.43 cm, the root mean squared error (RMSE) decreases from 7.08 cm to 5.98 cm, the standard deviation decreases from 8.19 cm to 7.07 cm, and the data utilization rate increases from 82.60% to 89.31%. In the snow surface conditions of ablation, accumulation and stabilization, the RMSE of improved algorithm decreases from 9.02 cm, 10.30 cm, 7.59 cm to 5.82 cm, 5.64 cm, 7.17 cm, and mean absolute error decreases from 6.77 cm, 7.52 cm, 7.00 cm to 5.39 cm, 4.72 cm, 6.73 cm, respectively.

  Conclusions  It can be seen that the proposed algorithm can provide more accurate and reliable snow depth even if in the conditions of ablation and accumulation.