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 Imaging 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 presen...
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2021
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ftdoajarticles:oai:doaj.org/article:2e01bb22e12c4e8a90e99a5e239993bc 2023-05-15T13:53:00+02:00 Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery S. Paul M. Huntemann 2021-03-01T00:00:00Z https://doi.org/10.5194/tc-15-1551-2021 https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc EN eng Copernicus Publications https://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-1551-2021 1994-0416 1994-0424 https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc The Cryosphere, Vol 15, Pp 1551-1565 (2021) Environmental sciences GE1-350 Geology QE1-996.5 article 2021 ftdoajarticles https://doi.org/10.5194/tc-15-1551-2021 2022-12-31T09:33:52Z The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging 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 or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual 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 and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods. Article in Journal/Newspaper Antarc* Antarctic Brunt Ice Shelf Ice Shelf Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Antarctic Brunt Ice Shelf ENVELOPE(-22.500,-22.500,-74.750,-74.750) The Cryosphere 15 3 1551 1565 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 S. Paul M. Huntemann Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging 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 or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual 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 and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods. |
format |
Article in Journal/Newspaper |
author |
S. Paul M. Huntemann |
author_facet |
S. Paul M. Huntemann |
author_sort |
S. Paul |
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 |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-15-1551-2021 https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc |
long_lat |
ENVELOPE(-22.500,-22.500,-74.750,-74.750) |
geographic |
Antarctic Brunt Ice Shelf |
geographic_facet |
Antarctic Brunt Ice Shelf |
genre |
Antarc* Antarctic Brunt Ice Shelf Ice Shelf Sea ice The Cryosphere |
genre_facet |
Antarc* Antarctic Brunt Ice Shelf Ice Shelf Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 15, Pp 1551-1565 (2021) |
op_relation |
https://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-1551-2021 1994-0416 1994-0424 https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc |
op_doi |
https://doi.org/10.5194/tc-15-1551-2021 |
container_title |
The Cryosphere |
container_volume |
15 |
container_issue |
3 |
container_start_page |
1551 |
op_container_end_page |
1565 |
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1766257943828758528 |