Abstract
Abstract. Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation.
Highlights
In the European Alps, the release of precipitated water to discharge is delayed by storage in snow and glaciers
Limited differences are observed between the orbits, which can be explained by the different local incidence angles, azimuth angles, and overpass times (06:00 LT for D, 18:00 LT for A)
Using a combination of cross-polarized and co-polarized backscatter as input to an empirical orbit-dependent change detection algorithm, S1 snow depth retrievals at 100 m, 500 m, and 1 km spatial and less-than-weekly temporal resolution are presented over the European Alps for the periods from August through April 2017–2019
Summary
In the European Alps, the release of precipitated water to discharge is delayed by storage in snow and glaciers. Despite the availability of these routine observations, limited attention has been paid to the use of Cband backscatter for snow monitoring after earlier satellite observations had shown a limited sensitivity (Rott and Nagler, 1993; Bernier and Fortin, 1998; Bernier et al, 1999; Shi and Dozier, 2000). Lievens et al (2019) demonstrated the sensitivity of S1 cross-polarized backscatter observations to dry snow accumulation and developed an empirical change detection algorithm to retrieve snow depth at 1 km spatial resolution over all mountain ranges in the Northern Hemisphere. Future research is recommended to further investigate the physical scattering mechanisms in snow at C-band, including the impacts of snow microstructure and stratigraphy, and to extend the validation over regions with different soil, vegetation, and snow conditions, using validation data at the matching scale of the satellite retrievals
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