Articles | Volume 13, issue 6
Earth Syst. Sci. Data, 13, 2723–2742, 2021
https://doi.org/10.5194/essd-13-2723-2021
Earth Syst. Sci. Data, 13, 2723–2742, 2021
https://doi.org/10.5194/essd-13-2723-2021

Data description paper 15 Jun 2021

Data description paper | 15 Jun 2021

Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning

Yi-Ran Wang and Xiao-Ming Li

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Xiaoming Li on behalf of the Authors (10 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Mar 2021) by Prasad Gogineni
RR by Anonymous Referee #1 (28 Mar 2021)
RR by Anonymous Referee #2 (17 Apr 2021)
ED: Publish as is (07 May 2021) by Prasad Gogineni
AR by Xiaoming Li on behalf of the Authors (14 May 2021)  Author's response    Manuscript
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Short summary
Sea ice cover is the most fundamental factor that indicates the underlying great changes in the Arctic. We propose novel sea ice cover data in high resolution of a few hundred meters by spaceborne synthetic aperture radar, which is more than 10 times that of the operational sea ice cover and concentration data. The method is based on a deep learning architecture of U-Net. We have been processing data acquired by Sentinel-1 since 2014 to obtain high-quality sea ice cover data in the Arctic.