# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 1 Introduction  
Snow cover is fundamental to the Earth's energy balance and moisture fluxes between the surface and the atmosphere, playing a crucial role in regulating the global climate [1]. Snow is also an essential component of both natural and human systems, exerting significant influence on water resources [2], agriculture [3, 4], ecology [5, 6, 7], socioeconomics [8, 9, 10], tourism [11, 12, 13], and hydropower generation [14, 15]. The critical importance of snow and the snow-covered area (SCA) in numerous system processes on Earth has prioritized the development of techniques for measuring SCA dynamics. These techniques include in situ sensors, hydrological modeling, and, most prominently, remote sensing [16, 17, 18]. Ground-based observations of SCA in alpine regions are relatively recent and sparsely distributeddue to the adverse conditions that complicate data collection. Likewise, despite great advances in SCA modeling in recent years [19; 20; 21], there remains a need for deeper physical understanding and proper calibration [22; 23]. Remote sensing programs have significantly increased the number of available data sources for SCA monitoring, enhancing our ability to track and analyze SCA dynamics on a broader scale [24; 25]. In particular, optical satellite sensors have been extensively used for snow observation worldwide [26; 27]. Among these sensors, the Moderate-Resolution Imaging Spectroradiometer (MODIS) holds a prominent position in hydrological studies due to its sufficiently high spatial resolution for global scale SCA monitoring, high temporal resolution, and relatively long data records [28; 29; 30]. SCA detection with MODIS achieves good overall accuracy for SCA detection (around 93%) [31], although its effectiveness can diminish in densely forested areas (around 71% in areas exceeding 50% of tree cover density) where the canopy obscures the underlying snowpack [32]. However, cloud cover conditions and dark night remain the main limitations of optical remote sensing, as they prevent the capture of reflectivity of the Earth's surface [33; 34; 35]. Consequently, prolonged periods cannot be observed with optical sensors, particularly in frequently clouded areas or regions with limited sunlight. An alternative to optical sensors for SCA monitoring is microwave remote sensing [36; 37; 38], which enables snow observation independently of weather and solar illumination conditions. Active microwave sensors, specifically Synthetic Aperture Radar (SAR), are the most commonly used for global-scale snow monitoring due to their high spatial resolution compared to passive microwave sensors [39]. The most widely used method for snow monitoring with SAR remote sensing is the backscattering-based method, commonly called Nagler's method [40; 41]. However, unlike optical remote sensing, the backscattering-based method is sensitive to snow state (dry or wet). Snow is a mixture of ice crystals, liquid water, and air. Dry snow is below 0 \({}^{\circ}\)C and, thus, only composed of ice particles and air. On the contrary, wet snow is at temperatures at or above 0 \({}^{\circ}\)C, so that its composition also includes significant quantities of liquid water [42]. At C- and X-band low microwave frequencies (4 to 8 GHz and 8 to 12 GHz, respectively), SAR remote sensing has demonstrated high potential for wet snow mapping [43]. At these frequencies, an increase in the liquid water content within the snowpack significantly reduces the radar backscattering signal in relation to snow-free and dry snow surfaces [44; 45]. In recent years, the European Space Agency's Copernicus program launched the Sentinel-1 mission, which is equipped with C-band SAR for land monitoring [46]. To leverage the capabilities of the Sentinel-1 constellation in cryosphere studies, the Copernicus Land Monitoring Service recently developed the High-Resolution Snow and Ice (HRS&I) project for wet snow monitoring in mountainous regions by means of the use of the backscattering-based method [40; 41].