Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation

Snow is an important water resource and plays a critical role in the hydrologic cycle. Accurate measurements of snow depth are needed by scientists to set up a more refined meteorology–hydrology model. Recently, the Global Navigation Satellite System Reflectometry (GNSS-R) has been developed and applied for snow depth monitoring, with low cost and high resolution. We propose an improved snow depth retrieval method using a combination of GNSS triple-frequency carrier phase. The topographic feature of the reflecting surface is considered for estimating the snow depth by using the density-based spatial clustering of applications with noise algorithm and normalization method. Observables from the GNSS station in Alaska, USA, are used to monitor snow depth and compared with the ground-truth measurements. Compared with the traditional triple-frequency snow depth retrieval method, the new approach has better performance for Galileo and BDS. The RMSE of the snow depth estimate reduces by nearly 40%, and the correlation coefficient increases from 0.93 to 0.97 for Galileo and from 0.91 to 0.95 for BDS, respectively. The research findings show no notable deviations on snow depth average estimation between Galileo and BDS observations compared to the GPS ones. Moreover, the solution with the proposed method results in improving spatial resolution due to the increasing number of satellites and better azimuth coverage.