@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 I. 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 I.
%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 I. 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.
