The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique is an effective method to monitor snow depth. The detrended signal-to-noise ratio (dSNR) series is analyzed by Lomb–Scargle periodogram (LSP) to extract the characteristic frequency, which can be converted to the snow depth. However, the dSNR data are greatly affected by noise in the observation environment, which leads to the abnormal characteristic frequency and low accuracy of snow depth retrieval. In order to reduce the influence of noise and to ensure the correct extraction of the characteristic frequency, we present an improved adaptive retrieval method for multi-constellation retrieval scenario. Firstly, the dSNR sequences are decomposed adaptively into several Singular Spectrum Components (SSCs) with different frequency scales by Singular Spectrum Decomposition (SSD). Then, the corresponding SSCs are selected, according to the empirical scope of snow depth, to reconstruct the “pure” dSNR series. Finally, the reconstructed signals are analyzed by LSP to derive the characteristic frequency, in order to obtain the snow depth. The multi-GNSS observations of site SG27 (Alaska, USA) and site P351 from Plate Boundary Observation network in a representative period from winter 2019 to spring 2020 were used to validate the proposed method. The snow depths were estimated from individual signals, individual constellations and multi-GNSS combination using both the traditional and the improved methods. The experimental results show that compared with the traditional method, the snow depth trend of the improved method is more consistent with the measured snow depth trend, especially in the early stage of snowfall. Furthermore, the proposed method shows a universal applicability to various signals of GPS, GLONASS, Galileo and BDS and the retrieval accuracy of all signals are improved in different degrees. When using multi-GNSS combination signals, the mean bias and RMSE of multi-GNSS snow depth retrieval at site SG27 are improved from 4.6 and 6.2 cm to 4.2 and 5.4 cm, respectively. The mean bias and RMSE at site P351 are improved from 10.5 and 12.4 cm to 9.5 and 11.5 cm, respectively.

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