Preliminary Study on Ocean Color Data Derived from Geostationary Ocean Color Satellite:GOCI in Bohai Sea and Yellow Sea
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- 英文论文题名：Preliminary Study on Ocean Color Data Derived from Geostationary Ocean Color Satellite:GOCI in Bohai Sea and Yellow Sea
- 论文作者：ZHAO Mengying ; LI Ying ; ZHANG Liang ; CUI Can ; ZHU Xueyuan
- 英文论文作者：ZHAO Mengying ; LI Ying ; ZHANG Liang ; CUI Can ; ZHU Xueyuan ; Dalian Ocean University ; Environmental Information Institute ; Dalian Maritime University
- 年：2015
- 作者机构：Dalian Ocean University;Environmental Information Institute,Dalian Maritime University;
- 英文论文关键词：GOCI ; HJ-1A ; chlorophyll-α ; inspection ; Bohai Sea and Yellow Sea ; diurnal variation
- 会议召开时间：2015-09-01
- 会议录名称：中国航海科技优秀论文集（2014）
- 语种：英文
- 分类号：P715.6
- 学会代码：PJJY
- 学会名称：上海浦江教育出版社有限公司
- 页数：12
- 文件大小：1422k
- 原文格式：O
摘要
This research evaluates the accuracy and quality of high temporal and spacial resolution satellite data in Bohai Sea and Yellow Sea,which derived from the first geostationary ocean color satellite in the world—GOCI(Geostationary Ocean Color Imager),launched by South Korea in June 2010.In this study,the process of atmospheric correction of GOCI is sketched firstly,and then the higher spacial resolution data derived from HJ-1A sensor and survey data is utilized to discuss and verify the accuracy of Chl-a data achieved from GOCI sensor in Bohai Sea and Yellow Sea.The satellite data-set is acquired from March to November in 2013.At last,monthly and diurnal variability of Chl-a in Bohai Sea and Yellow Sea is analyzed.The Chl-a match-up result indicates significant correlations,the relative error is about32%between GOCI and HJ-1A,correlation coefficient is 0.67,and RMSE is 0.16.The diurnal variation shows that Chl-a concentration gradually decreases in the morning and reaches the minimum at about 11:00(local time),keeps slowly increasing until 14:00 to the top.The lack of Chl-a data at coastal zone in satellite image is due to the interference of high suspended matter concentration,etc.Then we expect scholars can better improve the atmospheric correction of GOCI sensor,extract more near-shore data and more accurate data,which could provide data support for rapid change of natural disaster warning system and short/long term ocean environment monitoring.
This research evaluates the accuracy and quality of high temporal and spacial resolution satellite data in Bohai Sea and Yellow Sea,which derived from the first geostationary ocean color satellite in the world—GOCI(Geostationary Ocean Color Imager),launched by South Korea in June 2010.In this study,the process of atmospheric correction of GOCI is sketched firstly,and then the higher spacial resolution data derived from HJ-1A sensor and survey data is utilized to discuss and verify the accuracy of Chl-a data achieved from GOCI sensor in Bohai Sea and Yellow Sea.The satellite data-set is acquired from March to November in 2013.At last,monthly and diurnal variability of Chl-a in Bohai Sea and Yellow Sea is analyzed.The Chl-a match-up result indicates significant correlations,the relative error is about32%between GOCI and HJ-1A,correlation coefficient is 0.67,and RMSE is 0.16.The diurnal variation shows that Chl-a concentration gradually decreases in the morning and reaches the minimum at about 11:00(local time),keeps slowly increasing until 14:00 to the top.The lack of Chl-a data at coastal zone in satellite image is due to the interference of high suspended matter concentration,etc.Then we expect scholars can better improve the atmospheric correction of GOCI sensor,extract more near-shore data and more accurate data,which could provide data support for rapid change of natural disaster warning system and short/long term ocean environment monitoring.
This research evaluates the accuracy and quality of high temporal and spacial resolution satellite data in Bohai Sea and Yellow Sea,which derived from the first geostationary ocean color satellite in the world—GOCI(Geostationary Ocean Color Imager),launched by South Korea in June 2010.In this study,the process of atmospheric correction of GOCI is sketched firstly,and then the higher spacial resolution data derived from HJ-1A sensor and survey data is utilized to discuss and verify the accuracy of Chl-a data achieved from GOCI sensor in Bohai Sea and Yellow Sea.The satellite data-set is acquired from March to November in 2013.At last,monthly and diurnal variability of Chl-a in Bohai Sea and Yellow Sea is analyzed.The Chl-a match-up result indicates significant correlations,the relative error is about32%between GOCI and HJ-1A,correlation coefficient is 0.67,and RMSE is 0.16.The diurnal variation shows that Chl-a concentration gradually decreases in the morning and reaches the minimum at about 11:00(local time),keeps slowly increasing until 14:00 to the top.The lack of Chl-a data at coastal zone in satellite image is due to the interference of high suspended matter concentration,etc.Then we expect scholars can better improve the atmospheric correction of GOCI sensor,extract more near-shore data and more accurate data,which could provide data support for rapid change of natural disaster warning system and short/long term ocean environment monitoring.
引文
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[4]Gordon H.R,Wang,M.Surface-roughness consideration for atmospheric correction of ocean color sensors[J].I:The Rayleigh-scattering component,Applied optics,1992,31(21):4247-4260.
[5]Gordon H.R,Wang.M Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS:a preliminary algorithm[J].Applied optics,1992,33(3):443-452.
[6]SHEN Hong.Effect of Spectral Bandwidth and Spacial Scale of Multi-sensors Data on Remote Sensing of Ocean Color,2012.
[7]Ishizaka J,Sakuno Y.Validation of GOCI products and application to environmental monitoring of Japanese coastal waters,Proc.of 2~(nd)GOCI PI workshop&GOCI-II development workshop,25,2012.
[8]Itelson A.A,Schalle,J.F.,Hladik C.M Remote Chl-a retrieval in turbid,productive estuaries:Chesapeak Bay case study[J].Rem.Sens.Environ.,2007(109):464-472.
[9]QIAN Li.Research on Inversion Algorithms of Chlorophyll Concentration about Red Tides Monitoring in BOHAI Sea,2011.
[10]QIAN Li,LIU Wenling,ZHENG Xiaoshen.Spatial-temporal variation of Chlorophyll-a concentration in Bohai Sea based on MODIS[J].Marine Science Bulletin,2011,30(6):683-687.
[11]LIU Meng,SHEN Fang,GE Jianzhong,et al.Diurnal variation of suspended sediment concentration in Hangzhou Bay from geostationary satellite observation and its hydrodynamic analysis[J].Journal of Sediment Research,2013(1):7-13.
[12]Thiemann S.,Kaufman H.Lake water quality monitoring using hyperspectral airborne data-a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District,Germany[J].Rem.Sens.Environ.,2002(81):.228-237.
[13]PENG Xiangyi,SHEN Fang.Comparative Analysis of Suspended Particulate Matter Concentration in Yangtze Estuary Derived by Several Satellite Sensors[J].Infrared(monthly),2014,35(4):31-37.
[14]HAO Yanling.Detection of hourly variability of bio-optical properties in the Yellow River estray by Geostationary Satellite Ocean Color Images,2012.
[15]ZHANG Yanzhe.Study on Inversion Method of Chlorophyll Concentration in the Bohai Sea,2012.
[16]ZOU Yarong.Analysis Spatial Distribution Characteristics for Chla in Bohai Sea[J].Remote Sensing Information,2004(3):30-31.
[17]YANG Yu.Study on the Three Band Model for Chlorophll-a Concentration Retrieval in Lake Chaohu by Hyperspectral Data from HJ-1,2010.
[2]LI Chuanrong,TANG Lingli,HU Jian,et al.Potential Applications of HJ-1 Optical Satellites,Articles,2008,26(13):56-59.
[3]ZHANG Da,ZHENG Yuquan.Hyperspectral Remote Sensing and Its Development and Application Review[J].Optics&Optoelectronic Technology,2013,11(3):67—73.
[4]Gordon H.R,Wang,M.Surface-roughness consideration for atmospheric correction of ocean color sensors[J].I:The Rayleigh-scattering component,Applied optics,1992,31(21):4247-4260.
[5]Gordon H.R,Wang.M Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS:a preliminary algorithm[J].Applied optics,1992,33(3):443-452.
[6]SHEN Hong.Effect of Spectral Bandwidth and Spacial Scale of Multi-sensors Data on Remote Sensing of Ocean Color,2012.
[7]Ishizaka J,Sakuno Y.Validation of GOCI products and application to environmental monitoring of Japanese coastal waters,Proc.of 2~(nd)GOCI PI workshop&GOCI-II development workshop,25,2012.
[8]Itelson A.A,Schalle,J.F.,Hladik C.M Remote Chl-a retrieval in turbid,productive estuaries:Chesapeak Bay case study[J].Rem.Sens.Environ.,2007(109):464-472.
[9]QIAN Li.Research on Inversion Algorithms of Chlorophyll Concentration about Red Tides Monitoring in BOHAI Sea,2011.
[10]QIAN Li,LIU Wenling,ZHENG Xiaoshen.Spatial-temporal variation of Chlorophyll-a concentration in Bohai Sea based on MODIS[J].Marine Science Bulletin,2011,30(6):683-687.
[11]LIU Meng,SHEN Fang,GE Jianzhong,et al.Diurnal variation of suspended sediment concentration in Hangzhou Bay from geostationary satellite observation and its hydrodynamic analysis[J].Journal of Sediment Research,2013(1):7-13.
[12]Thiemann S.,Kaufman H.Lake water quality monitoring using hyperspectral airborne data-a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District,Germany[J].Rem.Sens.Environ.,2002(81):.228-237.
[13]PENG Xiangyi,SHEN Fang.Comparative Analysis of Suspended Particulate Matter Concentration in Yangtze Estuary Derived by Several Satellite Sensors[J].Infrared(monthly),2014,35(4):31-37.
[14]HAO Yanling.Detection of hourly variability of bio-optical properties in the Yellow River estray by Geostationary Satellite Ocean Color Images,2012.
[15]ZHANG Yanzhe.Study on Inversion Method of Chlorophyll Concentration in the Bohai Sea,2012.
[16]ZOU Yarong.Analysis Spatial Distribution Characteristics for Chla in Bohai Sea[J].Remote Sensing Information,2004(3):30-31.
[17]YANG Yu.Study on the Three Band Model for Chlorophll-a Concentration Retrieval in Lake Chaohu by Hyperspectral Data from HJ-1,2010.