Articles | Volume 15, issue 3
The Cryosphere, 15, 1551–1565, 2021
https://doi.org/10.5194/tc-15-1551-2021
The Cryosphere, 15, 1551–1565, 2021
https://doi.org/10.5194/tc-15-1551-2021

Research article 26 Mar 2021

Research article | 26 Mar 2021

Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

Stephan Paul and Marcus Huntemann

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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: Publish subject to minor revisions (review by editor) (10 Dec 2020) by Claude Duguay
AR by Stephan Paul on behalf of the Authors (19 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (19 Feb 2021) by Claude Duguay
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Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.