# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 2 Remote Sensing of Snow Cover  
One of the most used methods in the remote sensing of snow is the Normalized Difference Snow Index (NDSI) [20]. NDSI was developed to allow for the separation of cloud cover from snow cover using satellite sensors [21]. However, the extent of snow cover is routinely under-estimated after processing satellite images [22]; for example, in the Northern Hemisphere between November and April can have an error of 8%, without considering the error caused by cloud cover [23].  
Challenges in monitoring snow cover using satellite images include pixel size and temporal resolution of satellite measurements. The USGS Landsat program, the most commonly used source of remotely sensed data, has for four decades supplied images with a pixel size of 30 m \(\times\) 30 m once every 16 days for most locations [24]. Landsat's relatively low temporal resolution presents a significant limitation when monitoring changes in snow cover. On the other hand, Sentinel-2, the European Space Agency (ESA) satellites constellation, have a higher spatial and temporal resolution but have less than 10 years of image availability [25]. With daily temporal resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) images have coarse pixel sizes; when a MODIS pixel that is partially covered by 50% or more of snow is used for mapping it will be considered as fully snow-covered, overestimating the real value [26]. Furthermore, snow cover products derived from processed images by NASA-MODIS [27], ESA-Copernicus [28], and the Hydrological Satellite Facility project of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) [29] are available online. Issues such as cloud presence or low temporal and spatial resolutions have been addressed using different sensors together. For example, Sentinel-2 and Landsat-8 were used to obtain high-resolution snow cover maps [30], and [PERSON] et al. summarized the different methodologies to improve the generation of cloud-free MODIS snow cover products [31].  
Images from Unmanned Aircraft Systems (UASs) have been used to map snow cover with a better spatial resolution than satellite images. In one study, after performing an unsupervised classification, researchers found that the main cause for snow underestimation is shadows that fall on snow. The impact of shadows is strongest in the Red band and weakest in the Blue band [32].  
Oblique terrestrial images have been used to analyze mountainous terrain [33], to estimate snow albedo [34], monitor snow cover, and make other measurements such as snow depth [35; 36; 37; 38]. The use of this tool is suggested for snow cover because of its high reflectance values [39]. These images are also used for monitoring snow cover and its relationship with NDVI [40], the beginning of the growing season [41], and vegetation -phenological stages [42]. Moreover, near-surface images show potential for monitoring events related to snow and glaciers. Daily oblique images were used to monitor events such as rainfall that cannot be seen in satellite images [43] and to monitor snow dynamics in glaciers [44].  
Snow classification based on the Blue band threshold has been shown to be reliable compared with different satellite snow products, even when it has issues related to low sunlight and the presence of shadows [45; 46; 47; 48; 49; 50; 51; 52]. Although not evaluated in this study, [PERSON] et al. [53] proposed an algorithm that worked well in detecting FSC from images of rocky terrain at high altitude using a fixed terrestrial camera in Spain.