Fractional snow cover estimation through linear spectral unmixing of Sentinel-2 and Sentinel-3 optical satellite data using local endmembers

Precise snow cover estimations are relevant for many fields of research applications, such as for hydrological and meteorological modelling. Furthermore, snow plays an important role in hydropower management and flood prediction. Snow cover monitoring from satellite imagery has received increasing attention over the past decades. Nowadays, improvements in estimation methodologies and better availability of augmented satellite imagery provide an excellent basis for reliable estimations of fractional snow cover.

In this work we exploit the available spectral information of the Sentinel-2 MSI and Sentinel-3 OLCI for automatic estimation of fractional snow coverage. This is achieved through linear spectral unmixing with local endmembers. Similar implementation of methods that employ spectral unmixing use a reflectance model or a spectral library with pre-selected endmembers. Our approach selects the spectral endmembers from the data itself and applies them depending on the distance from the query point assuming spectral similarities of ground reflectance nearby. Endmembers are found through a pre-classification step based on conservative thresholds in combination with a similarity measure. The linear unmixing problem is solved several times for each query point using different combinations of endmembers detected in the vicinity of the query point; accounting for different illumination conditions and shaded areas. Finally, a careful selection of accurate fractional snow cover estimations is performed. This approach is globally applicable, adjusts to the local environment and illumination conditions and avoids costly endmember modelling or the provision of an external spectral library. The method was tested in different regions in the world using different satellite data including Sentinel-2, Landsat and Sentinel-3 OLCI and were inter-compared with snow information from other sources. In the presentation we will present the method, and examples of fractional snow cover maps. The performance of the method will be shown in comparison with other data and the limitations and capabilities will be discussed.