Snow depth derived from Sentinel-1 compared to in situ observations in northern Finland

Abstract. Seasonal snow in the northern regions plays an important role, providing water resources for both consumption and hydropower generation. Moreover, the snow changes in northern Finland during winter impact local agriculture, vegetation, tourism, and recreational activities. In this study we estimated snow depth using an empirical methodology applied to the dual polarisation of the Sentinel-1 synthetic aperture radar (SAR) images and compared our results with in situ measurements collected by automatic weather stations (AWSs) and snow courses in northern Finland. We applied an adapted version of the empirical methodology developed by Lievens et al. (2019) to retrieve snow depth, using the Sentinel-1 constellation between 2019 and 2022, and then compared our results to measurements from three automatic weather stations available over the same period. Overall, the Sentinel-1 snow depth retrievals were underestimated in comparison with the in situ measurements from the automatic weather stations. We found slightly different patterns for the different years, an overall correlation factor of 0.41, and a higher correlation in the 2020–2021 season (R=0.52). The high correlation between estimated and measured snow depth at the Inari Nellim location (R=0.81) reinforces the potential ability to derive snow changes in regions where in situ measurements of snow are currently lacking. Further investigation is still necessary to better understand how the physical properties of the snowpack influence the backscatter response over shallow-snow regions.