Abstract
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.
Highlights
The Arctic region, one of the regions with the most significant temperature increases, has exhibited a significant response to climate change [1], and the snow and glaciers in Svalbard have changed sharply in recent years [2,3]
In the field of snow depth estimation with the signal-to-noise ratio (SNR), Larson first proposed the feasibility of using SNR observations to detect the height of the reflecting surface [14] and found a good correlation between the snow depth obtained by GPS L1 SNR observations and ultrasonic snow depth measurements [15]
Larson applied snow depth estimation based on SNR observations to a complex terrain, and the results showed that GPS stations with poor observation quality, such as those in forests, were not suitable for snow depth measurement [17]
Summary
The Arctic region, one of the regions with the most significant temperature increases, has exhibited a significant response to climate change [1], and the snow and glaciers in Svalbard have changed sharply in recent years [2,3]. In the field of snow depth estimation with the signal-to-noise ratio (SNR), Larson first proposed the feasibility of using SNR observations to detect the height of the reflecting surface [14] and found a good correlation between the snow depth obtained by GPS L1 SNR observations and ultrasonic snow depth measurements [15]. Jacobson inferred both snow depth and snow density for a snow-covered ground reflector using GPS multipath signals and obtained an estimate of the snow water equivalent [16]. To better understand the relationship between the estimation method and snow-surface characteristics, the accuracy of the snow depth estimation during the accumulation, melting and stabilization periods were compared, and the impact of rainfall on snow depth estimation was analyzed with meteorological observations
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