A New GPS SNR-based Combination Approach for Land Surface Snow Depth Monitoring

Abstract

Snow is not only a critical storage component in the hydrologic cycle but also an important data for climate research; however, snowfall observations are only sparsely available. Signal-to-noise ratio (SNR) has recently been applied for sensing snow depths. Most studies only consider either global positioning system (GPS) L1 or L2 SNR data. In the current study, a new snow depth estimation approach is proposed using multipath reflectometry and SNR combination of GPS triple frequency (i.e. L1, L2 and L5) signals. The SNR combination method describes the relationship between antenna height variation and spectral peak frequency. Snow depths are retrieved from the SNR combination data at YEL2 and KIRU sites and validated by comparing it with in situ observations. The elevation angle ranges from 5° to 25°. The correlations for the two sites are 0.99 and 0.97. The performance of the new approach is assessed by comparing it with existing models. The proposed approach presents a high correlation of 0.95 and an accuracy (in terms of Root Mean Square Error) improvement of over 30%. Findings indicate that the new approach could potentially be applied to monitor snow depths and may serve as a reference for building multi-system and multi-frequency global navigation satellite system reflectometry models.