Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites
The opacity of clouds is the main problem for optical and thermal space-borne sensors, like the Moderate-Resolution Imaging Spectroradiometer (MODIS). Especially during polar nighttime, the low thermal contrast between clouds and the underlying snow/ice results in deficiencies of the MODIS cloud mas...
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ftmdpi:oai:mdpi.com:/2072-4292/7/5/5042/ 2023-08-20T04:02:31+02:00 Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites Stephan Paul Sascha Willmes Oliver Gutjahr Andreas Preußer Günther Heinemann agris 2015-04-23 application/pdf https://doi.org/10.3390/rs70505042 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs70505042 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 7; Issue 5; Pages: 5042-5056 MODIS polynyas sea ice clouds gap filling Text 2015 ftmdpi https://doi.org/10.3390/rs70505042 2023-07-31T20:43:11Z The opacity of clouds is the main problem for optical and thermal space-borne sensors, like the Moderate-Resolution Imaging Spectroradiometer (MODIS). Especially during polar nighttime, the low thermal contrast between clouds and the underlying snow/ice results in deficiencies of the MODIS cloud mask and affected products. There are different approaches to retrieve information about frequently cloud-covered areas, which often operate with large amounts of days aggregated into single composites for a long period of time. These approaches are well suited for static-nature, slow changing surface features (e.g., fast-ice extent). However, this is not applicable to fast-changing features, like sea-ice polynyas. Therefore, we developed a spatial feature reconstruction to derive information for cloud-covered sea-ice areas based on the surrounding days weighted directly proportional with their temporal proximity to the initial day of interest. Its performance is tested based on manually-screened and artificially cloud-covered case studies of MODIS-derived polynya area data for the polynya in the Brunt Ice Shelf region of Antarctica. On average, we are able to completely restore the artificially cloud-covered test areas with a spatial correlation of 0.83 and a mean absolute spatial deviation of 21%. Text Antarc* Antarctica Brunt Ice Shelf Ice Shelf Sea ice MDPI Open Access Publishing Brunt Ice Shelf ENVELOPE(-22.500,-22.500,-74.750,-74.750) Remote Sensing 7 5 5042 5056 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
MODIS polynyas sea ice clouds gap filling |
spellingShingle |
MODIS polynyas sea ice clouds gap filling Stephan Paul Sascha Willmes Oliver Gutjahr Andreas Preußer Günther Heinemann Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites |
topic_facet |
MODIS polynyas sea ice clouds gap filling |
description |
The opacity of clouds is the main problem for optical and thermal space-borne sensors, like the Moderate-Resolution Imaging Spectroradiometer (MODIS). Especially during polar nighttime, the low thermal contrast between clouds and the underlying snow/ice results in deficiencies of the MODIS cloud mask and affected products. There are different approaches to retrieve information about frequently cloud-covered areas, which often operate with large amounts of days aggregated into single composites for a long period of time. These approaches are well suited for static-nature, slow changing surface features (e.g., fast-ice extent). However, this is not applicable to fast-changing features, like sea-ice polynyas. Therefore, we developed a spatial feature reconstruction to derive information for cloud-covered sea-ice areas based on the surrounding days weighted directly proportional with their temporal proximity to the initial day of interest. Its performance is tested based on manually-screened and artificially cloud-covered case studies of MODIS-derived polynya area data for the polynya in the Brunt Ice Shelf region of Antarctica. On average, we are able to completely restore the artificially cloud-covered test areas with a spatial correlation of 0.83 and a mean absolute spatial deviation of 21%. |
format |
Text |
author |
Stephan Paul Sascha Willmes Oliver Gutjahr Andreas Preußer Günther Heinemann |
author_facet |
Stephan Paul Sascha Willmes Oliver Gutjahr Andreas Preußer Günther Heinemann |
author_sort |
Stephan Paul |
title |
Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites |
title_short |
Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites |
title_full |
Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites |
title_fullStr |
Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites |
title_full_unstemmed |
Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites |
title_sort |
spatial feature reconstruction of cloud-covered areas in daily modis composites |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2015 |
url |
https://doi.org/10.3390/rs70505042 |
op_coverage |
agris |
long_lat |
ENVELOPE(-22.500,-22.500,-74.750,-74.750) |
geographic |
Brunt Ice Shelf |
geographic_facet |
Brunt Ice Shelf |
genre |
Antarc* Antarctica Brunt Ice Shelf Ice Shelf Sea ice |
genre_facet |
Antarc* Antarctica Brunt Ice Shelf Ice Shelf Sea ice |
op_source |
Remote Sensing; Volume 7; Issue 5; Pages: 5042-5056 |
op_relation |
https://dx.doi.org/10.3390/rs70505042 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs70505042 |
container_title |
Remote Sensing |
container_volume |
7 |
container_issue |
5 |
container_start_page |
5042 |
op_container_end_page |
5056 |
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1774713015786012672 |