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

Paul, Stephan; Huntemann, Marcus

doi:https://doi.org/10.5194/tc-15-1551-2021

Articles | Volume 15, issue 3

https://doi.org/10.5194/tc-15-1551-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-15-1551-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 3

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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Final revised paper (published on 26 Mar 2021)
Preprint (discussion started on 13 Jul 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of manuscript TC-2020-159', Anonymous Referee #1, 03 Aug 2020
- AC1: 'Response to Reviewer 1', Stephan Paul, 02 Dec 2020
RC2: 'A review of tc-2020-15', Anonymous Referee #2, 06 Nov 2020
- AC2: 'Response to Reviewer 2', Stephan Paul, 02 Dec 2020

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

AR by Stephan Paul on behalf of the Authors (20 Feb 2021) Manuscript

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.