## 1 Introduction  
Snow is crucial in Alpine ecosystems, being a fundamental driver of multiple factors, both abiotic and biotic. Snow dynamics have a significant impact on ecosystems, as its seasonality affects the phenology of plants and animals, the ground thermal regime [1, 2], and it is an important water reservoir, pivotal to mountain hydrological regimes [3, 4]. Ona wider scale, due to its high albedo, it affects the global energy budget [5]. Snow also affects human welfare; on the one hand, by providing socio-economic services related to tourism and water supply [6; 7], and on the other hand, creating avalanche risk [8; 9]. Snow studies are also relevant for monitoring urban streams and lakes [10; 11]. For these reasons, in recent years, the study of snow distribution and its changes has become the focus of numerous studies [12; 13; 14; 15; 16; 17; 18; 19; 20]. Satellite remote sensing data can provide continuous spatio-temporal information on snow cover over long time series and on a global scale [21], overcoming the limitations of other types of observations, such as manual or automatic measurements by weather stations, in gathering information over wide and inaccessible areas [22]. The most widely used methods for extracting snow cover from satellite images are based on data from passive sensors [23], which include microwave and optical data. Microwave passive sensors can retrieve information related to snow depth (SD) and snow water equivalent (SWE), and are not affected by cloud cover or solar illumination, but are limited by a low spatial resolution (>1 km) [24]. SWE has been retrieved from the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) [25], the Scanning Multichannel Microwave Radiometer (SMMR) [26], the Microwave Radiation Imager (MWRI) [27], and the Special Sensor Microwave/Imager (SSM/I) [28] satellites. In contrast to microwave sensors, optical data offer long observation time series at higher spatial resolutions (\(\leq\)1 km) [24]. Most snow products are based on data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors [29] carried by the Terra and Aqua satellites, and the Advanced Very-High-Resolution Radiometer (AVHRR) sensors carried on NOAA's Polar Orbiting Environmental Satellites (POES) [30], providing daily coverage, but with low spatial resolution. In contrast, the Landsat [31] and, more recently, the Sentinel-2 constellations [32], provide high spatial resolution, but at the expense of revisit times of 16 days and 5 days, respectively. Optical sensors exploit the reflectance/absorption behavior of snow in the short-wave infrared (SWIR) and visible bands [33], summarized in the Normalized Difference Snow Index (NDSI) [34] to discriminate snow from other land cover classes and, in particular, from clouds [23]. The greatest limitation of optical sensors is their dependence on lighting and weather conditions. On the contrary, the use of Synthetic Aperture Radar (SAR) sensors to estimate snow and related variables depends on the roughness and dielectric properties of snow [21], and it is not affected by cloud cover and light conditions. Data collected from different types of sensors have been used in snow-related applications, varying in both temporal and spatial resolution, and type of wavelength emitted: the X-band satellites constellation COSMO-SkyMed [35], the satellites TanDEM-X [36] and TerraSAR-X [37]. More frequently used, due to the longer time series available and the C-band suitability in snow detection, are the Sentinel-1 [38; 39; 40], ERS [41], Envisat [42], and Radarsat [43] satellites. Lastly, the L-band Advanced Land Observation Satellite (ALOS) [44] has been employed in snow cover detection. Snow detection with SAR sensors has achieved great results in wet snow detection since it was first proposed to be used for this purpose [45], but dry snow detection is still a field under development [46].