Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry - Jiachun An; Pan Deng; Baojun Zhang; Jingbin Liu; Songtao Ai; Zemin Wang; Qiuze Yu (Remote Sensing, 2020)
Snow plays a critical role in hydrological monitoring and global climate change, especially in the Arctic region. As a novel remote sensing technique, global navigation satellite system interferometric reflectometry (GNSS-IR) has shown great potential for detecting reflector characteristics. In this study, a field experiment of snow depth sensing with GNSS-IR was conducted in Ny-Alesund, Svalbard, and snow depth variations over the 2014–2018 period were retrieved. First, an improved approach was proposed to estimate snow depth with GNSS observations by introducing wavelet decomposition before spectral analysis, and this approach was validated by in situ snow depths obtained from a meteorological station. The proposed approach can effectively separate the noise power from the signal power without changing the frequency composition of the original signal, particularly when the snow depth changes sharply. Second, snow depth variations were analyzed at three stages including snow accumulation, snow ablation and snow stabilization, which correspond to different snow-surface-reflection characteristics. For these three stages of snow depth variations, the mean absolute errors (MAE) were 4.77, 5.11 and 3.51 cm, respectively, and the root mean square errors (RMSE) were 6.00, 6.34 and 3.78 cm, respectively, which means that GNSS-IR can be affected by different snow surface characteristics. Finally, the impact of rainfall on snow depth estimation was analyzed for the first time. The results show that the MAE and RMSE were 2.19 and 2.08 cm, respectively, when there was no rainfall but 5.63 and 5.46 cm, respectively, when it was rainy, which indicates that rainfall reduces the accuracy of snow depth estimation by GNSS-IR.