Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions

Open water areas surrounded by sea ice significantly influence the ocean-ice-atmosphere interaction and contribute to Arctic climate change. Satellite altimetry can detect these ice openings and enables one to estimate sea surface heights and further altimetry data derived products. This study intro...

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Published in:Remote Sensing
Main Authors: Felix Müller, Denise Dettmering, Wolfgang Bosch, Florian Seitz
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2017
Subjects:
Online Access:https://doi.org/10.3390/rs9060551
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spelling ftmdpi:oai:mdpi.com:/2072-4292/9/6/551/ 2023-08-20T04:04:19+02:00 Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions Felix Müller Denise Dettmering Wolfgang Bosch Florian Seitz agris 2017-06-01 application/pdf https://doi.org/10.3390/rs9060551 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs9060551 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 9; Issue 6; Pages: 551 satellite altimetry Envisat SARAL unsupervised classification K-medoids Greenland Sea Fram Strait Text 2017 ftmdpi https://doi.org/10.3390/rs9060551 2023-07-31T21:07:53Z Open water areas surrounded by sea ice significantly influence the ocean-ice-atmosphere interaction and contribute to Arctic climate change. Satellite altimetry can detect these ice openings and enables one to estimate sea surface heights and further altimetry data derived products. This study introduces an innovative, unsupervised classification approach for detecting open water areas in the Greenland Sea based on high-frequency data from Envisat and SARAL. Altimetry radar echoes, also called waveforms, are analyzed regarding different surface conditions. Six waveform features are defined to cluster radar echoes into different groups indicating open water and sea ice waveforms. Therefore, the partitional clustering algorithm K-medoids and the memory-based classification method K-nearest neighbor are employed, yielding an internal misclassification error of about 2%. A quantitative comparison with several SAR images reveals a consistency rate of 76.9% for SARAL and 70.7% for Envisat. These numbers strongly depend on the quality of the SAR images and the time lag between the measurements of both techniques. For a few examples, a consistency rate of more than 90% and a true water detection rate of 94% can be demonstrated. The innovative classification procedure can be used to detect water areas with different spatial extents and can be applied to all available pulse-limited altimetry datasets. Text Arctic Climate change Fram Strait Greenland Greenland Sea Sea ice MDPI Open Access Publishing Arctic Greenland Remote Sensing 9 6 551
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic satellite altimetry
Envisat
SARAL
unsupervised classification
K-medoids
Greenland Sea
Fram Strait
spellingShingle satellite altimetry
Envisat
SARAL
unsupervised classification
K-medoids
Greenland Sea
Fram Strait
Felix Müller
Denise Dettmering
Wolfgang Bosch
Florian Seitz
Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions
topic_facet satellite altimetry
Envisat
SARAL
unsupervised classification
K-medoids
Greenland Sea
Fram Strait
description Open water areas surrounded by sea ice significantly influence the ocean-ice-atmosphere interaction and contribute to Arctic climate change. Satellite altimetry can detect these ice openings and enables one to estimate sea surface heights and further altimetry data derived products. This study introduces an innovative, unsupervised classification approach for detecting open water areas in the Greenland Sea based on high-frequency data from Envisat and SARAL. Altimetry radar echoes, also called waveforms, are analyzed regarding different surface conditions. Six waveform features are defined to cluster radar echoes into different groups indicating open water and sea ice waveforms. Therefore, the partitional clustering algorithm K-medoids and the memory-based classification method K-nearest neighbor are employed, yielding an internal misclassification error of about 2%. A quantitative comparison with several SAR images reveals a consistency rate of 76.9% for SARAL and 70.7% for Envisat. These numbers strongly depend on the quality of the SAR images and the time lag between the measurements of both techniques. For a few examples, a consistency rate of more than 90% and a true water detection rate of 94% can be demonstrated. The innovative classification procedure can be used to detect water areas with different spatial extents and can be applied to all available pulse-limited altimetry datasets.
format Text
author Felix Müller
Denise Dettmering
Wolfgang Bosch
Florian Seitz
author_facet Felix Müller
Denise Dettmering
Wolfgang Bosch
Florian Seitz
author_sort Felix Müller
title Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions
title_short Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions
title_full Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions
title_fullStr Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions
title_full_unstemmed Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions
title_sort monitoring the arctic seas: how satellite altimetry can be used to detect open water in sea-ice regions
publisher Multidisciplinary Digital Publishing Institute
publishDate 2017
url https://doi.org/10.3390/rs9060551
op_coverage agris
geographic Arctic
Greenland
geographic_facet Arctic
Greenland
genre Arctic
Climate change
Fram Strait
Greenland
Greenland Sea
Sea ice
genre_facet Arctic
Climate change
Fram Strait
Greenland
Greenland Sea
Sea ice
op_source Remote Sensing; Volume 9; Issue 6; Pages: 551
op_relation https://dx.doi.org/10.3390/rs9060551
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs9060551
container_title Remote Sensing
container_volume 9
container_issue 6
container_start_page 551
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