Factors Impacting Performance of the NDSI-Based Operational Snow Cover Monitoring Algorithm in Forested Landscapes

<p>In cold regions of the world with significant forest cover, a notable volume of precipitated snow resides under the forest cover. In such regions, snow is an abundant and valuable natural resource and assessing the winter extent of snow precipitation is particularly important for forecasting hydroelectric power potential, managing forests for maximizing the spring snowmelt yield, and monitoring animal habitats.</p><p>Forest presents challenging scenarios by obscuring much of the underlying snow over the forest floor from the view of the imaging spaceborne sensors. Moreover, due to the prevalence of mixed pixels, particularly in the forested landscapes, merely binarizing pixels into snow/snow-free can introduce errors while integrating the snow-covered area (SCA) information for hydro-climatological modeling. Therefore, the fractional snow-covered area (fSCA), which is a finer representation of the binary SCA and defines the snow-covered fraction of the pixel area, is a more reliable indicator.&#160;</p><p>The recently launched High Resolution Snow & Ice (HRSI) monitoring service by Copernicus allows exploitation of the high-resolution Sentinel-2 data by facilitating free distribution of NDSI-based operational snow cover maps. It also offers the feasibility to estimate the fractional snow cover (FSC) without the requirement of any end-member spectra. In this investigation, we assessed the performance of the NDSI-based operational snow cover area (SCA) monitoring algorithm and the associated FSC with respect to factors influencing the algorithm's performance. The investigation focused over test sites located in the northern Sierra Nevada mountain range in California, US and the central Spanish Pyrenees. The analyses indicated that terrestrial characteristics like tree cover density (TCD) and meteorological factors like incoming solar irradiance impacts the performance of the optical satellite-based snow cover monitoring algorithms. A strong dependence of the algorithm's performance on TCD (negatively correlated) and solar irradiance (positively correlated) was observed.</p>