Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes

Forest cover is a crucial factor that influences the performance of optical satellite-based snow cover monitoring algorithms. However, evaluation of such algorithms in forested landscapes is rare due to lack of reliable in situ data in such regions. In this investigation, we assessed the performance of the operational snow detection (SCA) and fractional snow cover estimation (FSC) algorithms employed by the Copernicus Land Monitoring Service for High-Resolution Snow & Ice Monitoring (HRSI) with a combination of Sentinel-2 and Landsat-7/8 satellite scenes, lidar-based, and in situ datasets. These algorithms were evaluated over test sites located in the forested mountainous landscape of the Pyrenees in Spain and the Sierra Nevada in the USA. Over the Pyrenees site, the effectiveness of snow cover detection was evaluated with respect to a time-series of in situ snow depth measurements logged over test plots with different aspects, canopy cover, and solar irradiance. Over the Sierra Nevada site, the impact of ground vegetation was assessed over the under canopy fractional snow cover retrievals using airborne lidar-derived fractional vegetation cover information. The analyses over the Pyrenees indicated a good accuracy of snow detection with the exception of plots with either dense canopy cover or insufficient solar exposure (shaded forested slope), or both. The operational HRSI algorithm yielded similar performances (25-30% RMSE) as the computationally intensive spectral unmixing approach while retrieving the subcanopy ground FSC over the Sierra Nevada site. It was observed that a more accurate lidar-derived tree cover density map did not improve the subcanopy FSC retrievals.

Automated summary

The study assessed the performance of snow detection and fractional snow cover estimation algorithms employed by Copernicus Land Monitoring Service in forested landscapes, revealing varying performance under different conditions.