GNSS Snow Depth Monitoring Using SNR Observations

Snow cover plays an important role in global climate regulation and hydrological cycle. However, conventional detection methods cannot achieve accurate detection of snow depth in large-scale. With the development of global positioning system multipath reflection (Global Navigation Satellite Systems Multipath Reflectometry, GNSS-MR) technology, the signal-to-noise ratio (Signal-to-Noise Ratio, SNR) data have been successfully used to detect soil moisture, sea level and other environmental parameters. To verify the difference and accuracy of the thickness of snow cover detected by GNSS using SNR observations at low elevation angles, in this paper, by increasing the elevation angle to obtain the long time series to invert snow depth. Using the GPS observations of the KIRU station in Sweden from January to May 2016 as a data source, the SNR data of L1 and L2 bands at this station are extracted. The snow depth inversion experiment is carried out at three sets of different low elevation angles, and the snow surface is extracted to the receiver antenna by Lomb-Scargle spectrum method. The inverted snow depth is compared with the situ measured snow depth. The results show that at the elevation angle range 0 degrees-20 degrees, 0 degrees-25 degrees and 0 degrees-30 degrees, the inverted snow depth of L1 and L2 bands are associated with the variations in situ measured values. By increasing the elevation angle to obtain the long time series, the inversion values are better at 0 degrees-25 degrees, 0 degrees-30 degrees, and the correlation coefficients are better than 0.93. At the snowless stage, the inverted values of L1 and L2 bands fluctuated around the measured values at different elevation angles.