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\begin{document}

\title{Classification of Arctic Sea Ice Surface Types During the Melt Season in
High-Resolution IceBridge Imagery}



\author[1,1]{Ellen Buckley}%
\author[2]{Sinéad Farrell}%
\author[3,3]{Kyle Duncan}%
\author[4,4]{Laurence Connor}%
\author[4,4]{John Kuhn}%
\author[5,5]{RoseAnne Dominguez}%
\author[2]{Sinéad Farrell}%
\affil[1]{University of Maryland College Park}%
\affil[2]{NOAA Laboratory for Satellite Altimetry, NCWCP}%
\affil[3]{University of Maryland}%
\affil[4]{NOAA Laboratory for Satellite Altimetry}%
\affil[5]{Universities Space Research Association Moffett Field}%


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  \date{May 10, 2024}


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\begin{abstract}
Melt ponds play an important role in the seasonal evolution of Arctic
sea ice. During the melt season, snow atop the sea ice begins to
metamorphose and melt, forming ponds on the ice. These ponds reduce the
albedo of the surface, allowing for increased solar energy absorption
and thus further melting of snow and ice. Analyzing the spatial
distribution and temporal evolution of melt ponds helps us understand
the sea ice processes that occur during the summer melt season. It has
been shown that the inclusion of melt pond parameters in sea ice models
increases the skill of predicting the summer sea ice minimum extent.
Previous studies have used remote sensing imagery to characterize
surface features and calculate melt pond statistics. Here we use new
observations of melt ponds obtained by the Digital Mapping System (DMS)
flown onboard NASA Operation IceBridge (OIB) during two Arctic summer
melt campaigns which surveyed thousands of kilometers of sea ice and
resulted in more than 45,000 images. One campaign was conducted in the
Beaufort Sea (July 2016), and one in the Lincoln Sea and the Arctic
Ocean north of Greenland (July 2017). Using these data we expect to
advance our understanding of the differences and similarities between
melt pond features on young, thin sea ice seen in the Beaufort Sea
versus those on multi-year ice. We have developed a pixel-based
classification scheme by considering the different RGB spectral values
associated with each surface type. We identify four sea ice surface
types (level ice, rubbled ice, open water, and melt ponds). The
classification scheme enables the calculation of parameters including
melt pond fraction, ice concentration, melt pond area, and melt pond
dimensions. We compare results with data from the Airborne Topographic
Mapper (ATM), a laser altimeter also operated during these OIB missions.
Given the extent over which the OIB data are available, regional
information may be derived. Leveraging existing satellite data products,
we examine whether the high-resolution airborne statistics are
representative of the region and can be scaled up for comparison against
satellite-derived parameters such as ice concentration and extent.%
\end{abstract}%



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