浮游植物类群遥感算法PHYSAT在台湾海峡的适用性研究
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摘要
浮游植物类群遥感是海色遥感的热点问题,关乎全球变化生态响应研究及有害藻华的辨识.针对目前广泛应用的浮游植物类群遥感全球算法PHYSAT,应用台湾海峡夏季表层浮游植物光合色素与SeaWiFS同步卫星遥感数据,探讨其区域适用性.结果显示两种主要类群(硅藻(Diatom)和聚球藻(Synechococcus))的遥感光谱异常(Ra)分布交错,且同一类群的Ra在不同航次、不同站位之间也存在差异,用PHYSAT算法阈值标准均不能得到有效识别.在建立归一化离水辐射率nLwref(λ,Chla)台湾海峡区域查找表的基础上,重新生成硅藻和聚球藻的Ra,不同类群的Ra依旧混杂.这可能与建立PHYSAT算法的标准海域和台湾海峡水体光学组分差异及台湾海峡的水体光学组分时空差异,尤其是颗粒后向散射系数bbp的变动有关.采用K-means和FCM(Fuzzy c-means)方法对443nm归一化的Ra进行聚类,准确率超过70%.该结果说明在类似台湾海峡的区域水体,浮游植物类群的遥感分辨可能需要更多考虑光谱谱形上的差异,而非如PHYSAT算法进行量值范围区分.
PHYSAT,one of the widely-accepted methods to detect multiple phytoplankton groups from space,was tested for its applicability in the Taiwan Strait(TWS)with 27 match-ups of in situ photosynthetic pigments collected during three cruises in summer and SeaWiFS daily data.Reflectance anomalies(Ra)spectra of the two dominant phytoplankton groups(Diatom and Synechococcus)in the TWS were completely mixed.Significant temporal and spatial variabilities in Ra distribution were also observed among samples of a specific phytoplankton group.The phytoplankton group in the TWS was not identified successfully using Ra with PHYSAT threshold and additional spectral criteria,even Ra based on the regional lookup table of nLwref(λ,Chla)for the TWS,which may be due to different bio-optical characteristics in waters,especially particulate backscattering coefficients(bbp).Clustering methods,K-means and FCM(Fuzzy c-means),were applied with Ra normalized at 443 nm.More than 70% of the samples were successfully identified.It seems that the remote sensing of phytoplankton groups may pay more attention to the difference in Raspectra shape rather than in the magnitude by PHYSAT method in regional areas such as the TWS.
PHYSAT,one of the widely-accepted methods to detect multiple phytoplankton groups from space,was tested for its applicability in the Taiwan Strait(TWS)with 27 match-ups of in situ photosynthetic pigments collected during three cruises in summer and SeaWiFS daily data.Reflectance anomalies(Ra)spectra of the two dominant phytoplankton groups(Diatom and Synechococcus)in the TWS were completely mixed.Significant temporal and spatial variabilities in Ra distribution were also observed among samples of a specific phytoplankton group.The phytoplankton group in the TWS was not identified successfully using Ra with PHYSAT threshold and additional spectral criteria,even Ra based on the regional lookup table of nLwref(λ,Chla)for the TWS,which may be due to different bio-optical characteristics in waters,especially particulate backscattering coefficients(bbp).Clustering methods,K-means and FCM(Fuzzy c-means),were applied with Ra normalized at 443 nm.More than 70% of the samples were successfully identified.It seems that the remote sensing of phytoplankton groups may pay more attention to the difference in Raspectra shape rather than in the magnitude by PHYSAT method in regional areas such as the TWS.
引文
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[24]黄邦钦,胡俊,柳欣,等.全球气候变化背景下浮游植物群落结构的变动及其对生物泵效率的影响[J].厦门大学学报(自然科学版),2011,50(2):402-410.
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[28]王磊,钟超,柳欣,等.夏季南海东北部和东海陆架浮游植物群落结构昼夜变化的比较研究[J].海洋学报,2013,35(6):170-177.
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[30] NAVARRO G,ALVAIN S,VANTREPOTTE V,et al.Identification of dominant phytoplankton functional types in the Mediterranean Sea based on a regionalized remote sensing approach[J].Remote Sensing of Environment,2014,152:557-575.
[31] BEN MUSTAPHA Z,ALVAIN S,JAMET C,et al.Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery:application to the detection of phytoplankton groups in open ocean waters[J].Remote Sensing of Environment,2014,146:97-112.
[32] KOHONEN T.Essentials of the self-organizing map[J].Neural Networks,2013,37:52-65.
[33] ANTOINE D,D'ORTENZIO F,HOOKER S B,et al.Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors(MERIS,SeaWiFS and MODIS-A)at an offshore site in the Mediterranean Sea(BOUSSOLE project)[J].Journal of Geophysical Research,2008,113:C07013.
[34]BAILEY S W,WERDELL P J.A multi-sensor approach for the on orbit validation of ocean color satellite data products[J].Remote Sensing of Environment,2006,102:12-23.
[35] SHANG S L,DONG Q,HU C M,et al.On the consistency of MODIS chlorophyll a products in the northern South China Sea[J].Biogeosciences,2014,11:269-280.
[2] TOTTERDELL I J,ARMSTRONG R A,DRANGE H,et al.Trophic resolution[C]∥Towards a Model of Ocean Biogeochemical Processes.Berlin:Springer,1993:71-92.
[3] BALCH W M,KILPATRICK K A,TREES C C.The 1991coccolithophore bloom in the central North Atlantic.Ⅰ.Optical properties and factors affecting their distribution[J].Limnology and Oceanography,1996,41(8):1669-1683.
[4] BROWN C W,YODER J A.Coccolithophorid blooms in the global ocean[J].Journal of Geophysical Research,1994,99:7467-7482.
[5] SHUTLER J D,GRANT M G,MILLER P I,et al.Coccolithophore bloom detection in the north east AtlanticusingSeaWiFS:algorithmdescription,application and sensitivity analysis[J].Remote Sensing of Environment,2010,114:1008-1016.
[6] SUBRAMANIAM A,CARPENTER E J,FALKOWSKI P G.Bio-optical properties of the marine diazotrophic cyanobacteria Trichodesmium spp.:Ⅱ.A reflectance model for remote sensing[J].Limnology and Oceanography,1999,44:618-627.
[7] SUBRAMANIAM A,BROWN C W,HOOD R R,et al.Detecting Trichodesmium blooms in SeaWiFS imagery[J].Deep Sea Research PartⅡ:Topical Studies in Oceanography,2002,49:107-121.
[8] WESTBERRY T K,SIEGEL D A,SUBRAMANIAM A.An improved bio-optical model for the remote sensing of Trichodesmium spp.blooms[J].Journal Geophysical Research,2005,110:C06012.
[9] CANNIZZARO J P,CARDER K L,CHEN F R,et al.A novel technique for detection of the toxic dinoflagellate,Karenia brevies,in the Gulf of Mexico from remotely sensed ocean color data[J].Continental Shelf Research,2008,28:137-158.
[10] SATHYENDRANATH S,WATTS L,DEVRED E,et al.Discrimination of diatoms from other phytoplankton using ocean-colour data[J].Marine Ecology Progress Series,2004,272:59-68.
[11] ALVAIN S,MOULIN C,DANDONNEAU Y,et al.Remote sensing of phytoplankton groups in case 1waters from global SeaWiFS imagery[J].Deep Sea Research PartⅠ:Oceanographic Research Papers,2005,52:1989-2004.
[12] ALVAIN S,MOULIN C,DANDONNEAU Y,et al.Seasonal distribution and succession of dominant phytoplankton groups in the global ocean:a satellite view[J].Global Biogeochemical Cycles,2008,22:GB3001.
[13] HIRATA T,HARDMAN-MOUNTFORD N M,BREWIN R J W,et al.Synoptic relationships between surface chlorophyll-a and diagnostic pigments specific to phytoplankton functional types[J].Biogeosciences,2011,8:311-327.
[14] PAN X J,WONG G G F,HO T Y,et al.Remote sensing of picophytoplankton distribution in the northern South China Sea[J].Remote Sensing of Environment,2013,128:162-175.
[15] ALVAIN S,LE QURC,BOPP L,et al.Rapid climatic driven shifts of diatoms at high latitudes[J].Remote Sensing of Environment,2013,132:195-201.
[16] MASOTTI I,BELVISO S,ALVAIN S,et al.Spatial and temporal variability of the dimethylsulfide to chlorophyll ratio in the surface ocean:an assessment based on phytoplankton group dominance determined from space[J].Biogeosciences,2010,7(10):3215-3237.
[17] ALVAIN S,LOISEL H,DESSAILLY D.Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters[J].Optics Express,2012,20:1070-1083.
[18]李月洋,孙群,王磊,等.利用PHYSAT方法反演南海浮游植物优势类群分布的季节变化[J].天津科技大学学报,2015,30(5):42-49.
[19] LIU F F,CHEN C Q.Remote sensing study of the seasonal distribution of phytoplankton groups in the South China Sea[C]∥2012 IEEE Geoscience and Remote Sensing Symposium.Munich:IEEE,2012:2563-2566.
[20] HONG H,ZHANG C,SHANG S,et al.Interannual variability of summer coastal upwelling in the Taiwan Strait[J].Continental Shelf Research,2009,29:479-484.
[21] SHANG S L,ZHANG C Y,HONG H S,et al.Hydrographic and biological changes in the Taiwan Strait during the1997-1998 El Ni1o winter[J].Geophysical Research Letters,2005,32:L11601.
[22]胡俊.台湾海峡南部浮游植物类群组成及其对上升流的响应研究[D].厦门:厦门大学,2009:123-147.
[23]王海黎,洪华生.近岸海域光合色素的生物标志作用研究Ⅰ.台湾海峡特征光合色素的分布及其对浮游植物类群结构的指示[J].海洋学报,2000,22(3):94-102.
[24]黄邦钦,胡俊,柳欣,等.全球气候变化背景下浮游植物群落结构的变动及其对生物泵效率的影响[J].厦门大学学报(自然科学版),2011,50(2):402-410.
[25] HUANG B Q,HU J,XU H Z,et al.Phytoplankton community at warm eddies in the northern South China Sea in winter 2003/2004[J].Deep Sea ResearchⅡ:Topical Studies in Oceanography,2010,57:1792-1798.
[26]谢中华.MATLAB统计分析与应用:40个案例分析[M].北京:北京航空航天大学出版社,2015:290-297.
[27] SHANG S,DONG Q,LEE Z,et al.MODIS observed phytoplankton dynamics in the Taiwan Strait:an absorption-based analysis[J].Biogeosciences,2011,8:841-850.
[28]王磊,钟超,柳欣,等.夏季南海东北部和东海陆架浮游植物群落结构昼夜变化的比较研究[J].海洋学报,2013,35(6):170-177.
[29] WEI J,LEE Z,SHANG S.A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments[J].Journal of Geophysical Research,2016,121:8189-8207.
[30] NAVARRO G,ALVAIN S,VANTREPOTTE V,et al.Identification of dominant phytoplankton functional types in the Mediterranean Sea based on a regionalized remote sensing approach[J].Remote Sensing of Environment,2014,152:557-575.
[31] BEN MUSTAPHA Z,ALVAIN S,JAMET C,et al.Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery:application to the detection of phytoplankton groups in open ocean waters[J].Remote Sensing of Environment,2014,146:97-112.
[32] KOHONEN T.Essentials of the self-organizing map[J].Neural Networks,2013,37:52-65.
[33] ANTOINE D,D'ORTENZIO F,HOOKER S B,et al.Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors(MERIS,SeaWiFS and MODIS-A)at an offshore site in the Mediterranean Sea(BOUSSOLE project)[J].Journal of Geophysical Research,2008,113:C07013.
[34]BAILEY S W,WERDELL P J.A multi-sensor approach for the on orbit validation of ocean color satellite data products[J].Remote Sensing of Environment,2006,102:12-23.
[35] SHANG S L,DONG Q,HU C M,et al.On the consistency of MODIS chlorophyll a products in the northern South China Sea[J].Biogeosciences,2014,11:269-280.