Snow depth monitoring is meaningful for climate analysis, hydrological research, and snow disaster prevention. Global navigation satellite system-reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing toward multiconstellation combined inversion. Aiming at the accuracy of snow depth inversion, this article introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that the VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network. The root-mean-square error (RMSE) of the inversion results is reduced by 20%–40% compared to the least-squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this article introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings and provides an important reference for further research on the GNSS-R technology.

中文翻译：

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基于多GNSS星座变分模态分解的GNSS-R雪深反演

雪深监测对于气候分析、水文研究和雪灾防治具有重要意义。全球导航卫星系统反射测量 (GNSS-R) 技术利用信噪比 (SNR) 调制频率与反射器高度之间的关系来监测积雪深度。现有的单星座研究取得了良好的进展，并逐渐向多星座组合反演方向发展。针对雪深反演的准确性，本文介绍了具有自适应高通滤波器特性的变分模态分解（VMD）算法对信噪比数据进行去趋势。KIRU站和P351站的实验结果表明，VMD算法适用于不同的星座，具有较好的信号分离效果。原位雪深由瑞典气象水文研究所 (SMHI) 和 SNOTEL 网络提供。与最小二乘拟合（LSF）算法相比，反演结果的均方根误差（RMSE）降低了20%~40%，相关系数也有很大提高。此外，考虑到气候站与反演区不存在重叠，本文引入最大光谱幅值作为另一个参考数据源，得到了基本一致的实验结论。在此基础上，将最大谱幅值作为熵法的输入变量，研究了组合策略的可行性。结果表明，该组合策略减少了少许反演误差，提高了雪深监测的时间分辨率。