@article{Gascoin-2020-Estimating,
title = "Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index",
author = "Gascoin, Simon and
Dumont, Zacharie Barrou and
Deschamps‐Berger, C{\'e}sar and
Marti, Florence and
Salgues, Germain and
L{\'o}pez‐Moreno, Juan Ignacio and
Revuelto, Jes{\'u}s and
Michon, Timoth{\'e}e and
Schattan, Paul and
Hagolle, Olivier",
journal = "Remote Sensing, Volume 12, Issue 18",
volume = "12",
number = "18",
year = "2020",
publisher = "MDPI AG",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G20-86001",
doi = "10.3390/rs12182904",
pages = "2904",
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 Pl{\'e}iades 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 {\mbox{$\times$}} tanh(a {\mbox{$\times$}} 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.",
}
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<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 \times tanh(a \times 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.</abstract>
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%0 Journal Article
%T Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index
%A Gascoin, Simon
%A Dumont, Zacharie Barrou
%A Deschamps‐Berger, César
%A Marti, Florence
%A Salgues, Germain
%A López‐Moreno, Juan Ignacio
%A Revuelto, Jesús
%A Michon, Timothée
%A Schattan, Paul
%A Hagolle, Olivier
%J Remote Sensing, Volume 12, Issue 18
%D 2020
%V 12
%N 18
%I MDPI AG
%F Gascoin-2020-Estimating
%X 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 \times tanh(a \times 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.
%R 10.3390/rs12182904
%U https://gwf-uwaterloo.github.io/gwf-publications/G20-86001
%U https://doi.org/10.3390/rs12182904
%P 2904
Markdown (Informal)
[Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index](https://gwf-uwaterloo.github.io/gwf-publications/G20-86001) (Gascoin et al., GWF 2020)
ACL
- Simon Gascoin, Zacharie Barrou Dumont, César Deschamps‐Berger, Florence Marti, Germain Salgues, Juan Ignacio López‐Moreno, Jesús Revuelto, Timothée Michon, Paul Schattan, and Olivier Hagolle. 2020. Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index. Remote Sensing, Volume 12, Issue 18, 12(18):2904.
