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\begin{document}

\title{Retrieval of Ocean Wave Heights from Spaceborne SAR over the Arctic
Marginal Ice Zone with a Neural Network}



\author[1]{Ke Wu}%
\author[2]{Xiaoming Li}%
\author[3]{Bingqing Huang}%
\affil[1]{Aerospace Information Research Institution, Chinese Academy of Sciences}%
\affil[2]{Aerospace Information Research Institute, Chinese Academy of Sciences (CAS)}%
\affil[3]{Aerospace Information Research Institute, Chinese Academy of Sciences}%


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  \date{May 21, 2024}


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\begin{abstract}
The twin Sentinel-1 (S1) satellites have been extensively acquiring
synthetic aperture radar (SAR) data in the Arctic, providing the unique
opportunity to obtain ocean dynamic parameters with both high spatial
resolution and wide swath coverage in the marginal ice zone (MIZ). In
this paper, we proposed a method for retrieving the ocean significant
wave height (SWH) from S1 SAR data in horizontal-horizontal (HH)
polarization based on a backpropagation neural network (BPNN). A total
of 4,273 scenes from S1 extra wide swath mode data acquired in the
Arctic were collocated with data from four radar altimeters (RA),
yielding 126,128 collocated data pairs. These data were separated into
training and testing datasets to develop a BPNN model for retrieving
SWH. Comparing the S1 retrieved SWH using the testing dataset with the
RA SWH yielded a bias of 0.17 m, a root-mean-square error of 0.71 m and
a scatter index of 23.05\% for SWH less than 10 m. The S1 retrieved SWH
were further compared with CFOSAT/SWIM data acquired in the Arctic
between August 2019 and May 2020 to validate the SWIM performance on
wave measurements at different beams.%
\end{abstract}%



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