Characteristic of GPS SNR and It’s Application for Snow Depth Monitoring Analysis

With the continuous construction of GNSS and the continuous improvement of GNSS reflection signal theory, GNSS-MR technology based on signal-to-noise ratio has gradually become an emerging field of GNSS for surface environmental monitoring. Based on the detailed analysis of the GPS signal-to-noise ratio (SNR) characteristics, this paper givens the GPS-MR technique based on SNR observation is proposed to detect the snow depth basic principle and calculation flow. In order to verify the effectiveness of SNR based on different frequencies for snow depth detection, this paper compares and analyzes the differences between L1C/A and L2P signals. On the basis of the above results, the GPS data of the AB33 station from 2011 to 2014 for four consecutive years were analyzed and compared with the snow depth of meteorological sensors. The experimental results show that the SNR-based GPS-MR algorithm can effectively obtain the snow depth from the GPS data, and the inversion result of L1C/A signal coincides well with the snow depth detection value of meteorological sensor, and it can detect the snow depth more effectively. GNSS-MR technology not only makes full use of the signal-to-noise ratio information, but also provides potential development space for GNSS technology for surface environment monitoring.