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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Online Access: | https://doi.org/10.5194/tc-15-1551-2021 https://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:2e01bb22e12c4e8a90e99a5e239993bc 2023-05-15T14:04:54+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 2021-03-01 https://doi.org/10.5194/tc-15-1551-2021 https://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc en eng Copernicus Publications doi:10.5194/tc-15-1551-2021 1994-0416 1994-0424 https://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc undefined The Cryosphere, Vol 15, Pp 1551-1565 (2021) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.5194/tc-15-1551-2021 2023-01-22T19:12:15Z 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 Unknown Antarctic Brunt Ice Shelf ENVELOPE(-22.500,-22.500,-74.750,-74.750) The Cryosphere 15 3 1551 1565 |
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English |
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geo envir 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 |
geo envir |
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://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf 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 |
doi:10.5194/tc-15-1551-2021 1994-0416 1994-0424 https://tc.copernicus.org/articles/15/1551/2021/tc-15-1551-2021.pdf https://doaj.org/article/2e01bb22e12c4e8a90e99a5e239993bc |
op_rights |
undefined |
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 |
_version_ |
1766276354098069504 |