Using global navigation satellite system (GNSS) to monitor snow depth helps scientists study the impacts of climate change and predict future climate patterns. In the process of extracting reflection signals from signal-to-noise ratio (SNR) data, traditional methods usually use low order polynomials for detrending terms. However, this traditional algorithm cannot completely eliminate the environmental noise in the SNR during signal decomposition, resulting in other noise sources still existing in the detrended SNR, which affects the inversion results. In order to make the inversion results more accurate, this paper proposes a robust empirical mode decomposition (REMD) based method. In signal decomposition, REMD is applied to improve the algorithm, and the correlation coefficient method is used to denoise the decomposed signal. The proposed algorithm is validated using the data from the U.S. Plate Boundary Observatory network SG27 site from winter 2016 to spring 2017 as the study data. The obtained experimental results are compared with the actual snow depth provided by Snowpack Telemetry. When the improved algorithm is used, the root mean square error and mean absolute error of the snow depth inversion at the SG27 site are improved, respectively.