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https://doi.org/10.5194/tc-2020-159
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-159
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Jul 2020

13 Jul 2020

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This preprint is currently under review for the journal TC.

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

Stephan Paul1 and Marcus Huntemann2,3 Stephan Paul and Marcus Huntemann
  • 1Department of Geography, Ludwig-Maximilian’s-University Munich, Munich, Germany
  • 2Department of Environmental Physics, University of Bremen, Bremen, Germany
  • 3Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate-Resolution Imageing Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of i) present clouds as sea ice and ii) open-water/thin-ice areas as clouds, which results in an underestimation of polynya area and subsequently derived information. Here, we present a novel machine-learning based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water/thin-ice areas in a given swath solely from thermal-infrared MODIS channels and additionally derived information. Compared to the reference MODIS sea-ice product, our data results in an overall increase of 31 % in annual swath-based coverage, attributed to an improved cloud-cover discrimination. Overall, higher spatial coverage results in a better sub-daily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

Stephan Paul and Marcus Huntemann

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Stephan Paul and Marcus Huntemann

Stephan Paul and Marcus Huntemann

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Latest update: 04 Aug 2020
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Short summary
Cloud cover in the polar regions is difficult to identify during night when using solely thermal infrared data, due to occurrences of warm clouds over cold sea ice as well as cold clouds of warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying thin-ice and open-water as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water/thin-ice, and cloud pixels in MODIS thermal-infrared satellite data.
Cloud cover in the polar regions is difficult to identify during night when using solely thermal...
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