# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 7 Conclusions  
This research used PhenoCam images in a semi-automatic way to estimate FSC at a site within Oakville, showing that the capabilities of the images are not only limited to monitor vegetation phenology. The results validated with two experts had average RMSEs of 5.02% and 10.32%. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. The number of cameras of the PhenoCam Network makes it possible to provide localized FSC estimates over the entire country, generating a database that can be used to validate and complement satellite data. Most of the images are classified using the Blue band, but thanks to the MoNIR band, the algorithm recognizes darker areas (shadows), and also, some days when the Blue band has a low reflectance value, the MoNIR band showed that it could be used to classify snow cover. Not all the cameras in the PhenoCam Network include the MoNIR band, and these sites could use the Blue band to estimate the FSC. During the study period (2015-2020), there was not a consistent pattern in the FSC in this area. Some years the first snowfall was on 1 December and other years it was on 9 October. The same happened with the snowmelt; in 2016, the last day with snow present was in March, while other years had their last snowfall in April. FSC is a parameter that depends on snowfall, temperature, energy flux, and their anomalies. Some limitations of this research were the field of view, the oblique view, and the vignetting effect; all of these related to the sensor. The field of view, which determines the area in the image, compared with other sensors, was small. The oblique view generates pixels that represent different real sizes; the closer the pixel, the smaller the area of ground represented. Vignetting happens when the borders of the image have darker colors compared with the center of the image. Figure 19: (**a**) Image in RGB, (**b**) FSC with shadow misclassification and (**c**) FSC adding the shadow pixels. A significant limitation was the subjectivity introduced into the accuracy assessment because we relied on the visual estimates of FSC made by two experts. Although we generally followed the accuracy assessment methods described by other research [50], we also added a pixel-based assessment, following methods described by [41]. In future studies, our goal is to tie our PhenoCam observations to corresponding data gathered by satellite sensors. We support the idea that datasets should be combined to achieve higher quality and less uncertainty in the remote sensing of snow cover [70]. Other limitations are external factors like weather conditions, land cover, and the presence of objects out of the study. On a sunny day, the scene is brighter, but there are also more shadows, and on cloudy days, the images have relatively low contrast. Blizzards and fog decrease visibility, and strong winds generate sastrugi. The land cover at Oakville is grassland, which during the senescence period has a golden color and sometimes is as bright as snow, creating misclassification. FSC is an important parameter that has to be monitored. The use of PhenoCam images and Python language, both free, allow researchers to keep developing scripts and to improve the existing work. This research was focused on one grassland site; future work should explore extending the findings to additional PhenoCam Network sites, working with other land covers and at different latitudes. Furthermore, future work should include the relationship between FSC and other parameters, such as positive and negative feedbacks related to climate change, the beginning and end of the growing season, and soil temperature and moisture. Data curation, A.C.B. ; Formal analysis, A.C.B. ; Methodology, A.C.B. and B.C.R. ; Supervision, B.C.R. ; Writing--original draft, A.C.B. ; Writing--review & editing, B.C.R. All authors have read and agreed to the published version of the manuscript.