Coastal upwelling observed by multi-satellite sensors
Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark patt...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, XiaoMing [verfasserIn] Li, XiaoFeng [verfasserIn] He, MingXia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Science in China - Heidelberg : Springer, 1997, 52(2009), 7 vom: Juli, Seite 1030-1038 |
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| Übergeordnetes Werk: |
volume:52 ; year:2009 ; number:7 ; month:07 ; pages:1030-1038 |
| Links: |
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| DOI / URN: |
10.1007/s11430-009-0088-x |
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| Katalog-ID: |
SPR019230893 |
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| 245 | 1 | 0 | |a Coastal upwelling observed by multi-satellite sensors |
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| 520 | |a Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. | ||
| 650 | 4 | |a coastal upwelling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SAR |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a AVHRR/SST |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a wind field retrieval |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, XiaoFeng |e verfasserin |4 aut | |
| 700 | 1 | |a He, MingXia |e verfasserin |4 aut | |
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10.1007/s11430-009-0088-x doi (DE-627)SPR019230893 (SPR)s11430-009-0088-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Li, XiaoMing verfasserin aut Coastal upwelling observed by multi-satellite sensors 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. coastal upwelling (dpeaa)DE-He213 SAR (dpeaa)DE-He213 AVHRR/SST (dpeaa)DE-He213 wind field retrieval (dpeaa)DE-He213 Li, XiaoFeng verfasserin aut He, MingXia verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 52(2009), 7 vom: Juli, Seite 1030-1038 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:52 year:2009 number:7 month:07 pages:1030-1038 https://dx.doi.org/10.1007/s11430-009-0088-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE AR 52 2009 7 07 1030-1038 |
| spelling |
10.1007/s11430-009-0088-x doi (DE-627)SPR019230893 (SPR)s11430-009-0088-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Li, XiaoMing verfasserin aut Coastal upwelling observed by multi-satellite sensors 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. coastal upwelling (dpeaa)DE-He213 SAR (dpeaa)DE-He213 AVHRR/SST (dpeaa)DE-He213 wind field retrieval (dpeaa)DE-He213 Li, XiaoFeng verfasserin aut He, MingXia verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 52(2009), 7 vom: Juli, Seite 1030-1038 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:52 year:2009 number:7 month:07 pages:1030-1038 https://dx.doi.org/10.1007/s11430-009-0088-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE AR 52 2009 7 07 1030-1038 |
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10.1007/s11430-009-0088-x doi (DE-627)SPR019230893 (SPR)s11430-009-0088-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Li, XiaoMing verfasserin aut Coastal upwelling observed by multi-satellite sensors 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. coastal upwelling (dpeaa)DE-He213 SAR (dpeaa)DE-He213 AVHRR/SST (dpeaa)DE-He213 wind field retrieval (dpeaa)DE-He213 Li, XiaoFeng verfasserin aut He, MingXia verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 52(2009), 7 vom: Juli, Seite 1030-1038 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:52 year:2009 number:7 month:07 pages:1030-1038 https://dx.doi.org/10.1007/s11430-009-0088-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE AR 52 2009 7 07 1030-1038 |
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10.1007/s11430-009-0088-x doi (DE-627)SPR019230893 (SPR)s11430-009-0088-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Li, XiaoMing verfasserin aut Coastal upwelling observed by multi-satellite sensors 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. coastal upwelling (dpeaa)DE-He213 SAR (dpeaa)DE-He213 AVHRR/SST (dpeaa)DE-He213 wind field retrieval (dpeaa)DE-He213 Li, XiaoFeng verfasserin aut He, MingXia verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 52(2009), 7 vom: Juli, Seite 1030-1038 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:52 year:2009 number:7 month:07 pages:1030-1038 https://dx.doi.org/10.1007/s11430-009-0088-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE AR 52 2009 7 07 1030-1038 |
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10.1007/s11430-009-0088-x doi (DE-627)SPR019230893 (SPR)s11430-009-0088-x-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Li, XiaoMing verfasserin aut Coastal upwelling observed by multi-satellite sensors 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. coastal upwelling (dpeaa)DE-He213 SAR (dpeaa)DE-He213 AVHRR/SST (dpeaa)DE-He213 wind field retrieval (dpeaa)DE-He213 Li, XiaoFeng verfasserin aut He, MingXia verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 52(2009), 7 vom: Juli, Seite 1030-1038 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:52 year:2009 number:7 month:07 pages:1030-1038 https://dx.doi.org/10.1007/s11430-009-0088-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.00 ASE AR 52 2009 7 07 1030-1038 |
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English |
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Enthalten in Science in China 52(2009), 7 vom: Juli, Seite 1030-1038 volume:52 year:2009 number:7 month:07 pages:1030-1038 |
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Enthalten in Science in China 52(2009), 7 vom: Juli, Seite 1030-1038 volume:52 year:2009 number:7 month:07 pages:1030-1038 |
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Li, XiaoMing @@aut@@ Li, XiaoFeng @@aut@@ He, MingXia @@aut@@ |
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2009-07-01T00:00:00Z |
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Li, XiaoMing |
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Li, XiaoMing ddc 550 bkl 38.00 misc coastal upwelling misc SAR misc AVHRR/SST misc wind field retrieval Coastal upwelling observed by multi-satellite sensors |
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550 ASE 38.00 bkl Coastal upwelling observed by multi-satellite sensors coastal upwelling (dpeaa)DE-He213 SAR (dpeaa)DE-He213 AVHRR/SST (dpeaa)DE-He213 wind field retrieval (dpeaa)DE-He213 |
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ddc 550 bkl 38.00 misc coastal upwelling misc SAR misc AVHRR/SST misc wind field retrieval |
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Coastal upwelling observed by multi-satellite sensors |
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coastal upwelling observed by multi-satellite sensors |
| title_auth |
Coastal upwelling observed by multi-satellite sensors |
| abstract |
Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. |
| abstractGer |
Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. |
| abstract_unstemmed |
Abstract Coastal upwelling phenomenon along the China coast in the Yellow Sea during August 2007 is studied using ENVISAT Advanced Synthetic Aperture Radar (ASAR) data, NOAA Advanced AVHRR series Sea Surface Temperature (SST) data, and NASA QuikSCAT Scatterometer ocean surface wind data. A dark pattern in an ASAR image is interpreted as coastal upwelling. This is because the natural biogenic slicks associated with coastal upwelling damp the Bragg waves on the sea surface and thus make the surface smoother. Most of the incoming radar energy is reflected in the forward direction. As a result, the radar backscatter signal is very weak. Analyzing the concurrent AVHRR SST image, we find that the dark pattern in the ASAR image is indeed corresponding to the low SST area. The wind retrieval in the slicks dominant region is biased due to the low Normalised Radar Cross Section (NRCS) associated with the coastal upwelling. We applied a SST correction to the NRCS values to improve the accuracy of wind retrieval from ASAR data. |
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7 |
| title_short |
Coastal upwelling observed by multi-satellite sensors |
| url |
https://dx.doi.org/10.1007/s11430-009-0088-x |
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true |
| author2 |
Li, XiaoFeng He, MingXia |
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Li, XiaoFeng He, MingXia |
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| doi_str |
10.1007/s11430-009-0088-x |
| up_date |
2024-07-04T00:44:04.932Z |
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1803607180506562560 |
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| score |
7.4010553 |