GNSS-IR Snow Depth Monitoring Based on a PCA Optimization Framework: Considering Independent and Common-Mode Features

Abstract

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an effective noncontact technique for monitoring snow depth, a critical indicator of global climate change. However, heterogeneous topography and inconsistent surface reflectivity introduce spatial variability into reflected signals, significantly degrading retrieval accuracy. This study proposes a multi-GNSS optimization framework based on principal component analysis (PCA) that isolates the dominant common-mode signal from diverse observations, thereby effectively extracting true snow depth variations. The snow depths are retrieved from continuous multiseason GNSS observations at stations P351 and P676 and validated against in situ measurements. The results indicate that the PCA-driven method integrates the dominant common-mode information from multisatellite observations, enabling more stable snow depth time series across GNSS constellations. At station P351, the root-mean-square error (RMSE) of the original retrievals ranges from 18.2 to 25.6 cm, whereas the proposed method reduces it to 10.8–11.8 cm. At station P676, the original performance is substantially worse, with RMSEs of 33.9–47.7 cm, while the improved method reduces the error to 12.8–13.3 cm. The PCA-driven framework also effectively captures snow-cover dynamics, achieving its highest accuracy during stable snow-covered periods, with an RMSE as low as 4 cm. In addition, DEM analysis suggests that topographic differences are associated with substantial residual post-PCA biases. This indicates that the new method can, to some extent, mitigate spatial correlation errors associated with terrain variations and scattering characteristics related to the movement of reflection points. Overall, the PCA framework can more fully leverage the observational advantages of multi-frequency and multisystem data, thereby better exploiting the monitoring potential of GNSS satellites, and providing a reliable solution for GNSS-IR snow depth monitoring in complex environments.