Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice

Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument us...

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Main Authors: M. Zygmuntowska, K. Khvorostovsky, V. Helm, S. Sandven
Format: Article
Language:English
Published: Copernicus Publications 2013-08-01
Series:The Cryosphere
Online Access:http://www.the-cryosphere.net/7/1315/2013/tc-7-1315-2013.pdf
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spelling doaj-96b09b604c4547f9ac4fdcb36e1b7cdc2020-11-24T23:40:43ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242013-08-01741315132410.5194/tc-7-1315-2013Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea iceM. ZygmuntowskaK. KhvorostovskyV. HelmS. SandvenSea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types have been analyzed. A set of parameters has been defined to characterize the differences in strength and width of the returned power waveforms. With a Bayesian-based method, it is possible to classify about 80% of the waveforms from three parameters: maximum of the returned power waveform, the trailing edge width and pulse peakiness. Furthermore, the maximum of the power waveform can be used to reduce the number of false detections of leads, compared to the widely used pulse peakiness parameter. For the pulse peakiness the false classification rate is 12.6% while for the power maximum it is reduced to 6.5%. The ability to distinguish between different ice types and leads allows us to improve the freeboard retrieval and the conversion from freeboard into sea ice thickness, where surface type dependent values for the sea ice density and snow load can be used.http://www.the-cryosphere.net/7/1315/2013/tc-7-1315-2013.pdf
collection DOAJ
language English
format Article
sources DOAJ
author M. Zygmuntowska
K. Khvorostovsky
V. Helm
S. Sandven
spellingShingle M. Zygmuntowska
K. Khvorostovsky
V. Helm
S. Sandven
Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
The Cryosphere
author_facet M. Zygmuntowska
K. Khvorostovsky
V. Helm
S. Sandven
author_sort M. Zygmuntowska
title Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
title_short Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
title_full Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
title_fullStr Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
title_full_unstemmed Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice
title_sort waveform classification of airborne synthetic aperture radar altimeter over arctic sea ice
publisher Copernicus Publications
series The Cryosphere
issn 1994-0416
1994-0424
publishDate 2013-08-01
description Sea ice thickness is one of the most sensitive variables in the Arctic climate system. In order to quantify changes in sea ice thickness, CryoSat-2 was launched in 2010 carrying a Ku-band radar altimeter (SIRAL) designed to measure sea ice freeboard with a few centimeters accuracy. The instrument uses the synthetic aperture radar technique providing signals with a resolution of about 300 m along track. In this study, airborne Ku-band radar altimeter data over different sea ice types have been analyzed. A set of parameters has been defined to characterize the differences in strength and width of the returned power waveforms. With a Bayesian-based method, it is possible to classify about 80% of the waveforms from three parameters: maximum of the returned power waveform, the trailing edge width and pulse peakiness. Furthermore, the maximum of the power waveform can be used to reduce the number of false detections of leads, compared to the widely used pulse peakiness parameter. For the pulse peakiness the false classification rate is 12.6% while for the power maximum it is reduced to 6.5%. The ability to distinguish between different ice types and leads allows us to improve the freeboard retrieval and the conversion from freeboard into sea ice thickness, where surface type dependent values for the sea ice density and snow load can be used.
url http://www.the-cryosphere.net/7/1315/2013/tc-7-1315-2013.pdf
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AT kkhvorostovsky waveformclassificationofairbornesyntheticapertureradaraltimeteroverarcticseaice
AT vhelm waveformclassificationofairbornesyntheticapertureradaraltimeteroverarcticseaice
AT ssandven waveformclassificationofairbornesyntheticapertureradaraltimeteroverarcticseaice
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