A Decade of QuikSCAT Scatterometer Sea Ice Extent Data
Abstract—Polar sea ice is an important input to global cli-mate models and is considered to be a sensitive indicator of cli-mate change. While originally designed only for wind estimation, radar backscatter measurements collected by wind scatterometers have proven useful for estimating the extent of...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.637.7042 2023-05-15T13:43:36+02:00 A Decade of QuikSCAT Scatterometer Sea Ice Extent Data Quinn P. Remund David G. Long The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.7042 http://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.7042 http://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf text ftciteseerx 2016-01-08T15:46:39Z Abstract—Polar sea ice is an important input to global cli-mate models and is considered to be a sensitive indicator of cli-mate change. While originally designed only for wind estimation, radar backscatter measurements collected by wind scatterometers have proven useful for estimating the extent of sea ice. During the Quick Scatterometer (QuikSCAT) mission, SeaWinds data were used to operationally map the sea ice extent. The resulting sea ice maps were used to mask near-surface winds to support SeaWinds ’ primary mission of measuring near-surface winds over the ocean. This paper describes the operational SeaWinds sea ice extent mapping algorithm, provides validation comparisons, and presents results from the ten-year data product. Starting with enhanced resolution horizontal polarization and vertical polar-ization backscatter images, the algorithm employs an iterative maximum-likelihood classifier with fixed thresholds to segment sea ice and open ocean pixels. Residual classification errors are reduced through binary image processing techniques and sea ice growth/retreat constraint methods. The algorithm results are compared with sea ice concentrations derived from Special Sensor Microwave/Imager data and with RADARSAT synthetic aperture radar imagery. The results suggest differences in the sensitivities of active and passive products given their channel sets and spe-cific algorithms. Derived sea ice extents over the full decade-long QuikSCAT mission data set are analyzed to show important trends in sea ice extent for the Antarctic and Arctic regions. Index Terms—Antarctica, Arctic, maximum likelihood (ML) detection, microwave sensors, microwave radiometry, QuikSCAT, radar remote sensing, sea ice, SeaWinds. I. Text Antarc* Antarctic Antarctica Arctic Sea ice Unknown Antarctic Arctic The Antarctic |
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Abstract—Polar sea ice is an important input to global cli-mate models and is considered to be a sensitive indicator of cli-mate change. While originally designed only for wind estimation, radar backscatter measurements collected by wind scatterometers have proven useful for estimating the extent of sea ice. During the Quick Scatterometer (QuikSCAT) mission, SeaWinds data were used to operationally map the sea ice extent. The resulting sea ice maps were used to mask near-surface winds to support SeaWinds ’ primary mission of measuring near-surface winds over the ocean. This paper describes the operational SeaWinds sea ice extent mapping algorithm, provides validation comparisons, and presents results from the ten-year data product. Starting with enhanced resolution horizontal polarization and vertical polar-ization backscatter images, the algorithm employs an iterative maximum-likelihood classifier with fixed thresholds to segment sea ice and open ocean pixels. Residual classification errors are reduced through binary image processing techniques and sea ice growth/retreat constraint methods. The algorithm results are compared with sea ice concentrations derived from Special Sensor Microwave/Imager data and with RADARSAT synthetic aperture radar imagery. The results suggest differences in the sensitivities of active and passive products given their channel sets and spe-cific algorithms. Derived sea ice extents over the full decade-long QuikSCAT mission data set are analyzed to show important trends in sea ice extent for the Antarctic and Arctic regions. Index Terms—Antarctica, Arctic, maximum likelihood (ML) detection, microwave sensors, microwave radiometry, QuikSCAT, radar remote sensing, sea ice, SeaWinds. I. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Quinn P. Remund David G. Long |
spellingShingle |
Quinn P. Remund David G. Long A Decade of QuikSCAT Scatterometer Sea Ice Extent Data |
author_facet |
Quinn P. Remund David G. Long |
author_sort |
Quinn P. Remund |
title |
A Decade of QuikSCAT Scatterometer Sea Ice Extent Data |
title_short |
A Decade of QuikSCAT Scatterometer Sea Ice Extent Data |
title_full |
A Decade of QuikSCAT Scatterometer Sea Ice Extent Data |
title_fullStr |
A Decade of QuikSCAT Scatterometer Sea Ice Extent Data |
title_full_unstemmed |
A Decade of QuikSCAT Scatterometer Sea Ice Extent Data |
title_sort |
decade of quikscat scatterometer sea ice extent data |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.7042 http://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf |
geographic |
Antarctic Arctic The Antarctic |
geographic_facet |
Antarctic Arctic The Antarctic |
genre |
Antarc* Antarctic Antarctica Arctic Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Arctic Sea ice |
op_source |
http://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.7042 http://www.mers.byu.edu/long/papers/TGARS2014_Remund.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766190961101111296 |