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\begin{document}

\title{On water and ice classification from Sentinel-2 imagery using machine
learning}



\author[1]{Rémi Jugier}%
\author[1]{Robin Cremese}%
\author[1]{Hugo Fournier}%
\author[1]{Núria Duran Gomez}%
\author[1]{Germain Salgues}%
\author[2]{Chloé Thenoz}%
\affil[1]{MAGELLIUM}%
\affil[2]{Magellium (France)}%


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  \date{May 21, 2024}


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\begin{abstract}
Accurate and dynamic mapping of water and ice surfaces is directly
useful to navigation and lake ice cover monitoring to study climate
change. Water and ice maps are also useful for various scientific
applications such as atmospheric correction of satellite imagery, remote
sensing of water quality, and as input data for hydrological, weather
and climate models. The existing literature shows that multi-spectral
satellite imagery, as provided by Sentinel-2 and Landsat-8, provides a
very effective means to discriminate between water, land, and ice.
However, most studies focus either on very specific cases (a specific
lake for instance), or on general cases but without complex and yet very
frequent cases such as turbid waters and salt lakes which can be
confused with snow and ice. The Copernicus High-Resolution Snow and Ice
Monitoring Service provides an operational Sentinel-2 ice and water
classification product at 20m resolution but with a lot of confusion on
the aforementioned cases. Using a database of 31 fully hand-labelled
Sentinel-2 L2A atmospherically corrected images, and machine learning
SVM and RandomForest methods, the current study shows that the
classification of land, water, ice, snow, turbid waters, salt lake
categories can be achieved with an accuracy over 93\%. It is also shown
that the atmospheric correction has little to no impact on the results,
as training and evaluating from L1C top of the atmosphere images instead
of L2A images yields very similar results. This last find is very useful
as it means that very accurate surface masks can now be provided to
atmospheric processors and may therefore considerably improve the
quality of atmospherically corrected images when compared to the current
usage of static masks.%
\end{abstract}%



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