Improvement of Snow Depth Inversion Derived From Terrain Tilt Correction and Multi-GNSS Measurements Using the Helmert Variance Component Estimation

As an essential part of the global water cycle, the effective detection of snow depth is of great significance to the study of climate change. Global navigation satellite system interferometric reflectometry (GNSS-IR) technology emerged as a new ground-based snow depth monitoring method. The principle is to invert the height of the reflection surface according to the signal-to-noise ratio (SNR) at a low elevation angle. However, the topographic relief in the reflection area of GNSS stations often dramatically impacts the accuracy of snow depth inversion. This study proposes a terrain tilt correction (TTC) algorithm based on different azimuth intervals to address it. Taking the P351 and P676 stations in the United States as examples, the snow depth inversion research of different systems is carried out. In the P351 station with gentle terrain, the TTC algorithm can maintain the stable accuracy. In the P676 station with steep terrain, the TTC algorithm can effectively compensate for the error caused by the landscape and significantly improve the accuracy of snow depth inversion. The experimental results of the two stations show that the snow depth inversion results of different systems after TTC are consistent with the in situ snow depth provided by the respective meteorological stations. Subsequently, a Helmert variance component estimation (HVCE) weighting algorithm is introduced for multisystem combinations based on different systems with different snow depth inversion accuracies, and the feasibility and stability of the combination strategy are investigated. Compared with the in situ snow depth, the correlation coefficient (
$R$
) of the combined snow depth at the P351 station is 0.91, the root-mean-square error (RMSE) is 19.7 cm, and the mean error (ME) is −12.2 cm. The snow depth obtained by the combination of the multisystem at the P676 station has an
$R$
of 0.95, an RMSE of 9.9 cm, and an ME of −4.9 cm. Compared with HVCE weighting before TTC, the RMSE is reduced by 53.3%, and the absolute value of ME is reduced by 74.2%. This study is not only of great significance for high-precision snow depth monitoring but also provides a necessary technical reference for further research on multisystem combined.