Articles | Volume 14, issue 8
https://doi.org/10.5194/tc-14-2629-2020
https://doi.org/10.5194/tc-14-2629-2020
Research article
 | 
20 Aug 2020
Research article |  | 20 Aug 2020

Classification of sea ice types in Sentinel-1 synthetic aperture radar images

Jeong-Won Park, Anton Andreevich Korosov, Mohamed Babiker, Joong-Sun Won, Morten Wergeland Hansen, and Hyun-Cheol Kim

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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
ED: Reconsider after major revisions (further review by editor and referees) (12 Dec 2019) by John Yackel
AR by Jeong-Won Park on behalf of the Authors (22 Jan 2020)  Author's response    Manuscript
ED: Publish subject to revisions (further review by editor and referees) (04 Feb 2020) by John Yackel
ED: Referee Nomination & Report Request started (03 Mar 2020) by John Yackel
RR by Anonymous Referee #1 (15 Mar 2020)
RR by Anonymous Referee #2 (16 Mar 2020)
RR by Anonymous Referee #3 (20 Mar 2020)
ED: Reconsider after major revisions (further review by editor and referees) (24 Mar 2020) by John Yackel
AR by Jeong-Won Park on behalf of the Authors (11 May 2020)  Author's response
ED: Referee Nomination & Report Request started (27 May 2020) by John Yackel
RR by Anonymous Referee #3 (09 Jun 2020)
RR by Anonymous Referee #1 (11 Jun 2020)
RR by Anonymous Referee #2 (12 Jun 2020)
ED: Publish subject to revisions (further review by editor and referees) (14 Jun 2020) by John Yackel
AR by Jeong-Won Park on behalf of the Authors (22 Jun 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (25 Jun 2020) by John Yackel
RR by Anonymous Referee #1 (26 Jun 2020)
RR by Anonymous Referee #2 (11 Jul 2020)
ED: Publish as is (14 Jul 2020) by John Yackel
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Short summary
A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.