Sea ice remote sensing using AMSR-E 89-GHz channels
Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution...
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description | Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA‐Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85‐GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18‐, 23‐, and 37‐GHz AMSR‐E data using weather filters. During two ship campaigns, the correlation of ASI, NASA‐Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR‐E period (2002–2006) between ASI and NASA‐Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies. |
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Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA‐Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85‐GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18‐, 23‐, and 37‐GHz AMSR‐E data using weather filters. During two ship campaigns, the correlation of ASI, NASA‐Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR‐E period (2002–2006) between ASI and NASA‐Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.</description><identifier>ISSN: 0148-0227</identifier><identifier>ISSN: 2169-9275</identifier><identifier>EISSN: 2156-2202</identifier><identifier>EISSN: 2169-9291</identifier><identifier>DOI: 10.1029/2005JC003384</identifier><language>eng</language><publisher>Blackwell Publishing Ltd</publisher><subject>Marine ; remote sensing ; sea ice</subject><ispartof>Journal of Geophysical Research. C. 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C. Oceans</title><addtitle>J. Geophys. Res</addtitle><description>Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA‐Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85‐GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18‐, 23‐, and 37‐GHz AMSR‐E data using weather filters. During two ship campaigns, the correlation of ASI, NASA‐Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR‐E period (2002–2006) between ASI and NASA‐Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.</description><subject>Marine</subject><subject>remote sensing</subject><subject>sea ice</subject><issn>0148-0227</issn><issn>2169-9275</issn><issn>2156-2202</issn><issn>2169-9291</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqN0U9PwjAUAPDGaCJBbn6AnYwHp69_1x7JAkMETEDDsem2TqdjwxWi-OkdzhhPag-vSd_vvZfmIXSK4RIDUVcEgI9DAEolO0AdgrnwCQFyiDqAmfSBkOAY9Zx7guYwLhjgDqILa7w8sV5tV9XGes6WLi8fvO1n7E8Xc3_gSeVHo3cveTRlaQt3go4yUzjb-7q76H44uAtH_uQ2ug77E98ITMFXkDHJGKRBEhgVpJxQEic8FimzXKZCZhhiFkspmiyLhRFSGVA0gThNCQS0i87avuu6etlat9Gr3CW2KExpq63TjRGSi_9BIIz8CbESgcLBHp7_AbFSXAoKDb1oaVJXztU20-s6X5l6pzHo_Wb0z800nLb8NS_s7lerx9E8bF7ZfojfVuVuY9--q0z9rJv_B1wvZ5HmOLqZyuVMD-kH7FuZIA</recordid><startdate>200802</startdate><enddate>200802</enddate><creator>Spreen, G.</creator><creator>Kaleschke, L.</creator><creator>Heygster, G.</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>KL.</scope></search><sort><creationdate>200802</creationdate><title>Sea ice remote sensing using AMSR-E 89-GHz channels</title><author>Spreen, G. ; Kaleschke, L. ; Heygster, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a6130-90f48440d7c7a97d5232bc5b6d4e58d68f10b4b886a974b6a689a093c0bdd2073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Marine</topic><topic>remote sensing</topic><topic>sea ice</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Spreen, G.</creatorcontrib><creatorcontrib>Kaleschke, L.</creatorcontrib><creatorcontrib>Heygster, G.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><jtitle>Journal of Geophysical Research. C. Oceans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Spreen, G.</au><au>Kaleschke, L.</au><au>Heygster, G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sea ice remote sensing using AMSR-E 89-GHz channels</atitle><jtitle>Journal of Geophysical Research. C. Oceans</jtitle><addtitle>J. Geophys. Res</addtitle><date>2008-02</date><risdate>2008</risdate><volume>113</volume><issue>C2</issue><epage>n/a</epage><issn>0148-0227</issn><issn>2169-9275</issn><eissn>2156-2202</eissn><eissn>2169-9291</eissn><abstract>Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA‐Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85‐GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18‐, 23‐, and 37‐GHz AMSR‐E data using weather filters. During two ship campaigns, the correlation of ASI, NASA‐Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR‐E period (2002–2006) between ASI and NASA‐Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.</abstract><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2005JC003384</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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title | Sea ice remote sensing using AMSR-E 89-GHz channels |
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