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

Simon Gascoin,Germain Salgues,Florence Marti,Timothée Michon,Jesús Revuelto,Juan Ignacio López-Moreno,Zacharie Barrou Dumont,César Deschamps-Berger,Olivier Hagolle,Paul Schattan

doi:10.3390/rs12182904

Abstract

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolutions on a global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrains using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectance 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 (RMSE) 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.

Highlights

The Global Observing System for Climate listed snow cover as one of the 50 essential climate variables to be monitored by satellite remote sensing to support the work of the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change [1]
We studied the feasibility of retrieving fractional snow cover (FSC) at 20 m resolution from Sentinel-2 normalized difference snow index (NDSI)
The FSC can be estimated from the Sentinel-2 NDSI using a sigmoid-shaped function

Summary

Introduction

The Global Observing System for Climate listed snow cover as one of the 50 essential climate variables to be monitored by satellite remote sensing to support the work of the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change [1]. Among the many variables that can be used to characterize the snow cover, the snow cover area is probably the most straightforward to retrieve from space [2,3]. A user requirements survey by the Cryoland consortium showed that snow cover area products were ranked as the most important among a list of operationally available remotely sensed snow products by the respondents [4]. The respondents emphasized the need for (i) short latency times in the product availability (shorter than 12 h); (ii) large spatial scale to cover entire mountain ranges like the Alps or even the whole of Europe; (iii) high spatial resolution down to 50 m resolution especially for road conditions and avalanche monitoring [4]

Results

Discussion

Conclusion