Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index

• DOI: 10.20944/preprints202007.0381.v1
• Publication Date: 2020-07-20T09:17:21Z
• Authors: Simon Gascoin; Zacharie Barrou D...; César Deschamps-B...; Florence Marti; Germain Salgues; Juan Ignacio Lópe...; Jesús Revuelto; Timothée Michon; Paul Schattan; Olivier Hagolle
• Keywords: environmental_sciences; 550; [SDE.MCG]Environmental Sciences/Global Changes; [SDV]Life Sciences [q-bio]; Science; Q; snow; snow cover area; fractional snow cover; Sentinel-2; snow; 15. Life on land; Snow cover area; 01 natural sciences; [SDV] Life Sciences [q-bio]; 13. Climate action; Snow; fractional snow cover; snow cover area; Fractional snow cover; Sentinel-2; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment; 0105 earth and related environmental sciences

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolution at global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrain using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectances after masking cloud and snow-free areas. The NDSI-FSC function is calibrated using Pléiades very high resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5×tanh(a×NDSI+b)+0.5 where a = 2.65 and b = -1.42 yielding a root mean square error of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.