GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With Multi-GNSS Constellations

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.