Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
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 prese...
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ftcopernicus:oai:publications.copernicus.org:tcd86364 2023-05-15T13:55:28+02:00 Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery Paul, Stephan Huntemann, Marcus 2020-07-13 application/pdf https://doi.org/10.5194/tc-2020-159 https://tc.copernicus.org/preprints/tc-2020-159/ eng eng doi:10.5194/tc-2020-159 https://tc.copernicus.org/preprints/tc-2020-159/ eISSN: 1994-0424 Text 2020 ftcopernicus https://doi.org/10.5194/tc-2020-159 2020-07-20T16:22:00Z 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. Text Antarc* Antarctic Sea ice Copernicus Publications: E-Journals Antarctic |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
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. |
format |
Text |
author |
Paul, Stephan Huntemann, Marcus |
spellingShingle |
Paul, Stephan Huntemann, Marcus Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
author_facet |
Paul, Stephan Huntemann, Marcus |
author_sort |
Paul, Stephan |
title |
Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
title_short |
Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
title_full |
Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
title_fullStr |
Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
title_full_unstemmed |
Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
title_sort |
improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime antarctic sea ice using modis thermal-infrared imagery |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-2020-159 https://tc.copernicus.org/preprints/tc-2020-159/ |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Sea ice |
genre_facet |
Antarc* Antarctic Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2020-159 https://tc.copernicus.org/preprints/tc-2020-159/ |
op_doi |
https://doi.org/10.5194/tc-2020-159 |
_version_ |
1766262083166404608 |