Introduction

Alpine snow cover plays an essential role in the water cycle of central Europe. It can be seen as a vital component for the seasonal water yield, as well as the groundwater recharge, and the freshwater supply for numerous people living downstream. In Austria, the snow cover time series from profiling stations shows a significant decrease in snow depth and winter accumulation, particularly during the core winter (Schöner et al., 2009). Based on model calculations Olefs et al. (2020) found 42 fewer snow-covered days and a reduced seasonal snow depth by 0.15 m in Austria between 1961/62 and 2019/20. Moreover, the strongest reductions were found at low elevation below 1,500 m a.s.l. The model results further suggest that snow-covered area in high-altitude catchments in the Austrian Alps decreased throughout the 21st century, which will impact seasonal runoff regimes (Hanzer et al., 2018).

At the same time, the snow cover in mountains can be challenging to observe because of the complexity of the terrain. Remote sensing is the only way to provide consistent spatial and temporal information about snow cover in hard-to-reach areas such as the alpine region. While the method proved its effectiveness in monitoring the snow cover extent with daily resolution using optical satellite observations in Austria (Tong et al., 2020), the authors highlight the limitations due to the coarse resolution. Using higher-resolution data, such as Sentinel-2, allows mapping snow cover at higher accuracy through the normalized difference snow index (NDSI) with thresholds that take into account the variability of snow-free surfaces (Ma & Zhang, 2022). Another limitation of current optical methods is connected to the frequent cloud cover blocking the view of the ground. On the other hand, Synthetic Aperture Radar (SAR) can ensure the continuous monitoring of snow cover throughout the year. C-band SAR is sensitive to liquid water in snow (Brangers et al., 2024), and observations can be used with field measurements and snowpack models to discriminate effectively between wet and dry phases throughout the entire snow season (Lund et al., 2022).

The present research designs and tests a concept for snow cover monitoring based on the combined observations from Sentinel-1 and Sentinel-2. The new algorithm was tested in Hohe Tauern National Park in Austria between 2018 and 2025. The study uses Principal Component Analysis to combine a composite of eight bands of optical reflectance and SAR backscatter measurements. The component is used to separate pixels of snow-covered and snow-free land cover. Further, the classification follows the hierarchical snow pixel-based approach and groups dry and wet snow phases at different altitudinal zones (three classes: Non-Snow, Dry Snow, and Wet Snow).