Summary
This study develops and validates a method for mapping fractional snow cover across large areas using Sentinel-2 satellite data and a simple empirical function based on the normalized difference snow index. The authors calibrate the NDSI–FSC relationship using high-resolution Pleiades imagery and evaluate it against multiple independent datasets including satellite imagery, camera photographs, lidar scans, and crowdsourced measurements. The resulting sigmoid-shaped function achieves a root mean square error of 25% with a 95% confidence interval of 38%, demonstrating practical utility for global snow cover monitoring at 20 m resolution.
Regional applicability
This is a global satellite remote sensing methodology not specific to any region. The approach is directly applicable to United Kingdom mountain and upland areas (Scottish Highlands, Lake District, Snowdonia, Pennines) where snow cover monitoring is relevant for hydrological forecasting, avalanche hazard assessment, and climate monitoring; however, the method's performance in areas with mixed vegetation and topographic complexity would require validation against UK-specific conditions.
Key measures
Fractional snow cover (FSC) derived from NDSI; root mean square error (RMSE) of 25%; 95% confidence interval of 38% on FSC retrievals; sigmoid function parameters (a = 2.65, b = -1.42)
Outcomes reported
The study calibrated and evaluated an empirical function to estimate fractional snow cover (FSC) in open terrain using Sentinel-2 satellite imagery and the normalized difference snow index (NDSI). FSC retrievals were validated against multiple independent datasets including very high resolution satellite imagery, time-lapse photography, terrestrial lidar, and in situ measurements.
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