A Revised Snow Cover Algorithm to Improve Discrimination between Snow and Clouds: A Case Study in Gran Paradiso National Park

Snow cover plays an important role in biotic and abiotic environmental processes, as well human activities, on both regional global scales. Due to the difficulty of situ data collection vast inaccessible areas, use optical satellite imagery represents a useful support for snow mapping. At present, several operational algorithms products are available. Even though most them offer up-to-daily time scale, they do not provide sufficient spatial resolution studies requiring high detail. By contrast, Let-It-Snow (LIS) algorithm can produce high-resolution maps, based normalized-difference index (NDSI) digital elevation model. The latter is introduced define threshold value altitude, below which presence excluded. In this study, we revised LIS by introducing new parameter, shortwave infrared (SWIR) band, modifying overall workflow, such that cloud mask selection be used input. has been applied case study Gran Paradiso National Park. Unlike previous studies, also compared performance original modified cover, order evaluate their effectiveness discriminating between clouds. Ground collected meteorological stations equipped with gauges solarimeters were validation purposes. changes improve upon classification accuracy obtained (i.e., up 89.17 from 80.88%). producer’s user’s values (89.12 95.03%, respectively) larger than those (76.68 93.67%, respectively), thus providing more accurate map.