Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

Abstract

Abstract. Seasonal snow in mountain regions is an essential water resource. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) from regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We demonstrate the ability of the Sentinel-1 mission to monitor weekly snow depth at sub-kilometer (100 m, 300 m and 1 km) resolutions over the European Alps, for 2017–2019. Sentinel-1 backscatter observations, especially for the cross-polarization channel, 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 a change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. For dry snow conditions, the 1 km Sentinel-1 retrievals have a spatio-temporal correlation (R) of 0.87 and mean absolute error (MAE) of 0.17 m compared to in situ measurements across 743 sites in the European Alps. A slight reduction in performance is observed for the retrievals at 300 m (R = 0.85 and MAE = 0.18 m) and 100 m (R = 0.79 and MAE = 0.21 m). The results demonstrate the ability of Sentinel-1 to provide regional snow estimates at an unprecedented resolution 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 resources management, flood forecasting and numerical weather prediction.

Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

“…Data availability. The Sentinel-1 snow depth retrievals at 500 m and 1 km spatial and less-than-weekly temporal resolution are available online at https://ees.kuleuven.be/project/c-snow (Lievens et al, 2021).…”

Brooks Range Perennial Snowfields: Extent Detection from the Field and via Satellite

“…This approach did not work well in our study, potentially because VV ref and VH ref were subtracted from VV summer and VH summer , respectively, rather than used in a ratio, as was done by Nagler et al (2016). In this study, the two backscatter values were subtracted, as was done by Lievens et al (2021). This resulted in backscatter values that were easier to interpret and threshold.…”

“…Artifacts from the mountainous terrain created strong noise signals that made it impossible to discern backscatter changes among the perennial snowfields when using the Nagler approach, even after the radiometric slope correction from Vollrath et al (2020) was applied. Therefore, the polarization "ratio" calculated was the difference between subtracting the January composite VV ref from the single image VV summer and the VH ref from the VH summer , similar to the methodology outlined and implemented by Lievens et al (2021). The VV backscatter ratio (difference) created a stronger contrast in the SAR images than that of the VH, so the VV portion of the change algorithm was given more weight and multiplied by a constant factor of 3.…”

Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

“…Therefore, merging ascending and descending orbit data is challenging in different heterogeneous environments [10]. Many studies have normalized SAR data to the same incidence angle, thus achieving data merging with a view to reducing the observation period of the data [53]. In terms of incidence angle, a smaller incidence angle (>35-40 • ) increases the depth through the snow and maximizes the scattering contribution from the snow; while, when the incidence angle is steeper (<30 • ), it reduces the attenuation and makes the contribution from ground scattering in the signal more pronounced [54].…”