A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E

We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the...

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Published in:Remote Sensing
Main Authors: David Bröhan, Lars Kaleschke
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2014
Subjects:
Online Access:https://doi.org/10.3390/rs6021451
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spelling ftmdpi:oai:mdpi.com:/2072-4292/6/2/1451/ 2023-08-20T04:03:55+02:00 A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E David Bröhan Lars Kaleschke agris 2014-02-18 application/pdf https://doi.org/10.3390/rs6021451 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs6021451 https://creativecommons.org/licenses/by/3.0/ Remote Sensing; Volume 6; Issue 2; Pages: 1451-1475 sea ice remote-sensing leads lead orientation image analysis Text 2014 ftmdpi https://doi.org/10.3390/rs6021451 2023-07-31T20:35:56Z We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the progressive probabilistic Hough transform to automatically infer lead positions and orientations from daily AMSR-E satellite observations. Because the progressive probabilistic Hough transform often detects an identical lead several times the algorithm clusters neighboring leads that belong to one lead position. A first comparison of automatically detected lead positions and orientations with manually detected lead positions and orientations reveals that 57% of the reference leads are correctly determined. Around 11% of automatically detected leads are located where no reference lead occurs. The automatically detected lead orientations are distributed slightly differently from the reference lead orientations. A second comparison of automatically detected leads in the Fram Strait to leads in a wide swath mode Advanced Synthetic Aperture Radar scene shows a good agreement. We provide an Arctic-wide time series of lead orientations for winters from 2002 to 2011. For example, while a lead orientation of 110° with respect to the Greenwich meridian prevails in the Fram Strait, lead orientations in the Beaufort Sea are more isotropically distributed. We find significant preferred lead orientations almost everywhere in the Arctic Ocean when averaged over the entire AMSR-E time series. Text Arctic Arctic Ocean Beaufort Sea Fram Strait Sea ice MDPI Open Access Publishing Arctic Arctic Ocean Greenwich Remote Sensing 6 2 1451 1475
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic sea ice
remote-sensing
leads
lead orientation
image analysis
spellingShingle sea ice
remote-sensing
leads
lead orientation
image analysis
David Bröhan
Lars Kaleschke
A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
topic_facet sea ice
remote-sensing
leads
lead orientation
image analysis
description We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the progressive probabilistic Hough transform to automatically infer lead positions and orientations from daily AMSR-E satellite observations. Because the progressive probabilistic Hough transform often detects an identical lead several times the algorithm clusters neighboring leads that belong to one lead position. A first comparison of automatically detected lead positions and orientations with manually detected lead positions and orientations reveals that 57% of the reference leads are correctly determined. Around 11% of automatically detected leads are located where no reference lead occurs. The automatically detected lead orientations are distributed slightly differently from the reference lead orientations. A second comparison of automatically detected leads in the Fram Strait to leads in a wide swath mode Advanced Synthetic Aperture Radar scene shows a good agreement. We provide an Arctic-wide time series of lead orientations for winters from 2002 to 2011. For example, while a lead orientation of 110° with respect to the Greenwich meridian prevails in the Fram Strait, lead orientations in the Beaufort Sea are more isotropically distributed. We find significant preferred lead orientations almost everywhere in the Arctic Ocean when averaged over the entire AMSR-E time series.
format Text
author David Bröhan
Lars Kaleschke
author_facet David Bröhan
Lars Kaleschke
author_sort David Bröhan
title A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
title_short A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
title_full A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
title_fullStr A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
title_full_unstemmed A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E
title_sort nine-year climatology of arctic sea ice lead orientation and frequency from amsr-e
publisher Multidisciplinary Digital Publishing Institute
publishDate 2014
url https://doi.org/10.3390/rs6021451
op_coverage agris
geographic Arctic
Arctic Ocean
Greenwich
geographic_facet Arctic
Arctic Ocean
Greenwich
genre Arctic
Arctic Ocean
Beaufort Sea
Fram Strait
Sea ice
genre_facet Arctic
Arctic Ocean
Beaufort Sea
Fram Strait
Sea ice
op_source Remote Sensing; Volume 6; Issue 2; Pages: 1451-1475
op_relation https://dx.doi.org/10.3390/rs6021451
op_rights https://creativecommons.org/licenses/by/3.0/
op_doi https://doi.org/10.3390/rs6021451
container_title Remote Sensing
container_volume 6
container_issue 2
container_start_page 1451
op_container_end_page 1475
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