# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 4 Conclusions  
GNSS-IR is a new remote sensing technique for snow depth estimation. In this study, long-term GNSS observations from 2014 to 2018 in Ny-Alesund were collected and used to retrieve snow depths with GNSS-IR. First, an improved approach based on wavelet decomposition was proposed to improve the accuracy of snow depth estimation with GNSS-IR. Due to the good denoising effect of wavelet decomposition, the noise power and signal power can be accurately separated without changing the frequency composition in the original signal, which can significantly improve the accuracy and reliability of the estimations. The errors with the proposed method were lower, and the data utilization was better. The validity of the proposed approach is particularly obvious when the snow depth changes sharply. Second, the accuracy of snow depth estimation during the three periods of the accumulation, ablation and stabilization of snow cover is discussed in this paper. The results show that snow depth estimation is more accurate during the snow-stabilization period, and more complicated snow surfaces are present during the snow-accumulation period. The difference in estimation accuracy  
Figure 10: Estimated snow depths in situations with (**a** and **b**) and without (**c** and **d**) rainfall. Red and green lines represent the measured and estimated snow depths, respectively. Blue lines denote the amounts of rainfall during this period.  
during different snow-cover periods is quite obvious. Finally, combined with the local meteorological observations at Ny-Alesund, the influence of rainfall on the accuracy of GNSS-IR estimation was studied for the first time. The results show that the accuracy of snow depth estimation without rainfall is significantly higher than that with rainfall.  
With the development of global navigation satellite systems, future research can focus on combinations of observations at different frequencies and from different navigation satellite constellations. In addition, the snow water equivalent can be further calculated from snow depth variation once the snow density is determined. Future work could also focus on the influences of snow type, snow wetness, grain size and snow density on GNSS-IR snow depth estimations to improve the retrieval precision further.  
[PERSON], [PERSON] and [PERSON] conceived of the study, supervised the experiments and improved the manuscript. [PERSON], [PERSON], [PERSON] and Q.Y. contributed to the analysis and discussion. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41531069, 41941010 and 41874031, and the Fundamental Research Funds for the Central Universities of China, grant number 2042019k0220, and supported in part by the Science and Technology Fund of Hubei Province grant number 2018 AAA070 and 2018 CFA007.  
The authors are grateful to the Norwegian Meteorological Institute for providing meteorological observations in Ny-Alesund. We also greatly appreciate the field work support from the Chinese Arctic and Antarctic Administration and Polar Research Institute of China.  
The authors declare no conflict of interest.