黄海叶绿素及初级生产力的遥感估算
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摘要
水色遥感提供的数据资料具有水平范围大和瞬时近乎同步的特点，能有效地监测海洋水色要素的区域分布特征和动态变化规律。利用卫星数据反演叶绿素浓度来评价海洋环境污染，尤其是预测、探测赤潮等，是一种有效的方法。而利用海洋遥感研究海洋碳储量，有助于了解海洋中碳的生物地球化学循环，从而了解全球的碳循环以及气候变化，对海洋生态、全球碳循环研究有重要意义。
本研究使用MODIS影像数据结合实测光谱数据，利用统计分析和神经网络等数学方法，建立黄海二类水体叶绿素浓度反演模型。结果表明，非线性模型略优于线性模型；神经网络模型优于统计模型；最后以改进算法的Erf-BP神经网络模型建立的叶绿素浓度估算方法最好，相关研究为叶绿素浓度遥感反演以及深入的数据分析提供了理论基础。
将叶绿素浓度遥感估算模型应用到多时相遥感数据，得到研究区域不同时相叶绿素浓度分布图，并分析黄海海域叶绿素浓度的时空变化特征。对研究海区2010年各个月份叶绿素浓度分布统计得出，2010年各月叶绿素浓度的最小值为0.320mg/m~3，最大值达到了23.696mg/m~3，各个月份叶绿素浓度均值最大达到了3.371mg/m~3。叶绿素的空间分布特征为中心海区叶绿素浓度低于边缘海，北部海区叶绿素浓度低于南部海区，这与前人研究结果基本吻合。通过目视判读，讨论了不同时期叶绿素浓度的空间和时间变化规律。结果表明，春季和夏初，整个海区叶绿素浓度水平较高；受制于营养盐的影响，夏季，研究海区叶绿素浓度含量略有降低；秋季，研究海区内叶绿素浓度水平又呈现增加的趋势；冬季，受制于水温影响，叶绿素浓度整体水平偏低。根据叶绿素浓度分布图的统计特征，将叶绿素浓度分为9个等级，使用重心的方法，进行时空变化分析。分别统计不同时相每个等级叶绿素的重心位置，通过绘制不同时相重心的散点图，分析重心随时间变化规律，研究叶绿素时空变化特征和规律。
结合研究区域及数据特点选择VGPM模型估算研究海区初级生产力。利用MODIS温度、Kd490、SeaWIFS PAR以及反演的叶绿素浓度数据作为VGPM模型输入参数，计算研究海区的初级生产力，并分析其季节性变化特征。
结果表明，黄海海域初级生产力季节性变化明显，具有双峰特征，即呈现出春季>秋季>夏季>冬季的趋势。对比2010年2月、4月、6月、8月、10月和12月的初级生产力遥感估算结果，6月初级生产力最高，初级生产力平均水平为1342.802mgC/m~2d；4月次之，初级生产力平均水平为1157.993mgC/m~2d；10月第三，为1092.379mgC/m~2d；冬季最小，12月和2月，研究海区初级生产力仅为873.609mgC/m~2d和786.622mgC/m~2d。
此外，本文讨论了离水辐射亮度的空间分布特征，水-气界面处辐射能量透射系数的空间分布特征，以及入射光和观测几何的变化对离水辐射亮度和水-气界面处辐射能量的透射系数的影响情况。结果表明，恰水面之下水体下行、上行辐照度是一个与入射太阳天顶角有关的变量，随着入射太阳天顶角的增加，恰水面之下下行辐照度和上行辐照度明显衰减。离水辐射亮度的空间分布特征与入射光太阳天顶角有关，离水辐射亮度与观测天顶角之间呈现先增后减的趋势，这种趋势可以用一元三次方程表达。入射光太阳天顶角对透射系数不产生影响，观测方位角和表面粗糙度亦不影响透射系数，但透射系数随着观测天顶角的变化而呈现出明显的衰减特征。透射系数的衰减与观测天顶角可用线性方程来表达。
选择合适的数学方法，利用遥感手段，能够较好的反演二类水体叶绿素浓度及其他水色要素信息。结合遥感数据大空间尺度和长时间尺度的特点，实现对整个研究海区水色要素时空变化分析。VGPM模型可以用来估算研究海区初级生产力，使用根据特定研究区域建立的叶绿素浓度反演算法，提高叶绿素浓度估算精度；采用光衰减系数计算真光层深度，在一定程度上降低了初级生产力的估算误差，避免了VGPM模型对二类水体的不适用性问题。这些研究为开展利用遥感手段进行海洋光学调查和海洋生物地球化学调查和研究提供了参考。
Color remote sensing data has a large horizontal scale and near-synchronizationinstantaneous characteristics, it can monitor the regional distribution and dynamic changes of theocean color elements effetively. Inversed chlorophyll concentration using satellite data is aneffective way to assess the pollution of the marine environment, especially to predict and detectthe red tide, and so on. Using ocean remote sensing data to estimate the ocean carbon storage ishelpful to understand the marine biogeochemical carbon cycle and then the global carbon cycleand climate change, which is significant to study marine ecology and global carbon cycle.
In this study, the mathematical methods including statistical analysis and neural network areused to establish the chlorophyll concentration inversion model for the Yellow sea case II watersusing MODIS images and measured spectral data. The results show that the nonlinear model isslightly better than the linear model; neural network model is superior to the statistical model;the best method for chlorophyll concentration estimation is the Erf-BP neural network model thatis an improved algorithm of traditional back propagation neural network, this work provides atheoretical basis for the chlorophyll concentration estimation and data analysis in-depth.
Remote sensing model for chlorophyll concentration estiamtion is applied to themulti-temporal remote sensing data to gets the different temporal chlorophyll concentrationdistribution in order to analyse the spatial and temporal variation of the Yellow sea chlorophyllconcentration. The statistical results derived from the each month chlorophyll concentrationmaps of2010year show that the minimum, maximum and mean chlorophyll concentration ofeach month is0.320mg/m~3,23.696mg/m~3and3.371mg/m~3respectively. The chlorophyllconcentration is lower in the center areas than that in the coastal areas, and is lower in thenorthern yellow sea than that in the southern areas. This result is consistent with the previousstudies. The spatial and temporal variations of the chlorophyll in different periods were discussedby visual interpretation. It is found that the chlorophyll concentration level of the whole studyarea is higher in spring and early summer due the nutrient control and has a slightly reduce insummer and an increasing trend in autumn; in winter, because of the effect of the temperature,the chlorophyll concentration is lower than other seasons.The chlorophyll concentration isdivided into nine levels according to the statistical characteristics of the chlorophyllconcentration maps, and its temporal and spatial variation is analysed using the gravity centermethod. The gravity center positions of the multi-temporal chlorophyll concentration on eachlevel were calculated and ploted to study the chlorophyll temporal and spatial variationcharacteristics in study area.
Combined with the features of the study area and characteristics of the data, the VGPM(Vertically Generalized Production Model, VGPM) model is selected to estimate the ocean primary productivity, MODIS temperature data, MODIS Kd490(diffuse attenuation coefficientsof down-welling irradiance at490nm) data, SeaWIFS PAR (Photosynthetically Active Radiation,PAR) data and inversed chlorophyll concentration data were used as input parameters for theVGPM model to calculate the primary productivity. And then its seasonal variationcharacteristics were analysed.
The results show that the seasonal variation of primary productivity in the yellow sea isobvious with a bimodal characteristics, which present a trend of spring> autumn> summer>winter. Contrasting the primary productivity in February2010, April, June, August, October andDecember, the maximum value is in June with the value of1342.802mgC/m~2d, followed byApril with the value of1157.993mgC/m~2d, October with the value of1092.379mgC/m~2d, theminimum primary productivity presents in winter with the value of873.609mgC/m~2d and786.622mgC/m~2d in December and February, respectively.
In addition, the spatial distribution characteristics of the water-leaving radiance and thetransmission coefficient of the upward radiance just below the surface, as well as effects of roughsurface, incident light and observation geometry on these distribution characteristics werediscussed. The results shows that downward and upward irradiances just below the surfacedecreased by the increases of solar zenith angle, with a linear relations to the cosine value ofsolar zenith angle. Spatial distribution characteristic of the water-leaving radiance is obvious andhas nothing to do with wind-driven rough surface. A simple cubic equation was developed hereto fit the changes of water-leaving radiance with viewing zenith angle. On the flat surface, thetransmission coefficient of the upward radiance just below the surface does not change with theviewing azimuth angle, however, reduces by the viewing zenith angle increasing and is linearto its square tangent value. On the rough surface, the transmission coefficient of the upwardradiance just below the surface changes little with the viewing azimuth angle but obviously withthe viewing zenith angle. The relationship also exists between transmission coefficient and thesquare tangent value of viewing zenith angle.
Choosing suitable mathematical methods, it is better to retrieve chlorophyll concentrationand other ocean color information of Case II waters based on remote sensing skills. Combinedwith the characteristics of large spatial scales and long temporal scales of remote sensing data, itcan achieve the temporal and spatial variation of ocean color elements in study area. The VGPMmodel can be used to estimate the primary productivity, using the specific chlorophyllconcentration retrieval algorithm based on a specific study area to reduce the estimation error ofthe chlorophyll concentration. Light attenuation coefficient was used to calculate the euphoticdepth, which can some extent reduce the estimation error of the primary productivity and avoidthe limitation of applicability of VGPM model in Case II waters. These studies provide a reference for carrying out the marine optical survey and marine biogeochemical investigationand study by remote sensing tools.
本研究使用MODIS影像数据结合实测光谱数据，利用统计分析和神经网络等数学方法，建立黄海二类水体叶绿素浓度反演模型。结果表明，非线性模型略优于线性模型；神经网络模型优于统计模型；最后以改进算法的Erf-BP神经网络模型建立的叶绿素浓度估算方法最好，相关研究为叶绿素浓度遥感反演以及深入的数据分析提供了理论基础。
将叶绿素浓度遥感估算模型应用到多时相遥感数据，得到研究区域不同时相叶绿素浓度分布图，并分析黄海海域叶绿素浓度的时空变化特征。对研究海区2010年各个月份叶绿素浓度分布统计得出，2010年各月叶绿素浓度的最小值为0.320mg/m~3，最大值达到了23.696mg/m~3，各个月份叶绿素浓度均值最大达到了3.371mg/m~3。叶绿素的空间分布特征为中心海区叶绿素浓度低于边缘海，北部海区叶绿素浓度低于南部海区，这与前人研究结果基本吻合。通过目视判读，讨论了不同时期叶绿素浓度的空间和时间变化规律。结果表明，春季和夏初，整个海区叶绿素浓度水平较高；受制于营养盐的影响，夏季，研究海区叶绿素浓度含量略有降低；秋季，研究海区内叶绿素浓度水平又呈现增加的趋势；冬季，受制于水温影响，叶绿素浓度整体水平偏低。根据叶绿素浓度分布图的统计特征，将叶绿素浓度分为9个等级，使用重心的方法，进行时空变化分析。分别统计不同时相每个等级叶绿素的重心位置，通过绘制不同时相重心的散点图，分析重心随时间变化规律，研究叶绿素时空变化特征和规律。
结合研究区域及数据特点选择VGPM模型估算研究海区初级生产力。利用MODIS温度、Kd490、SeaWIFS PAR以及反演的叶绿素浓度数据作为VGPM模型输入参数，计算研究海区的初级生产力，并分析其季节性变化特征。
结果表明，黄海海域初级生产力季节性变化明显，具有双峰特征，即呈现出春季>秋季>夏季>冬季的趋势。对比2010年2月、4月、6月、8月、10月和12月的初级生产力遥感估算结果，6月初级生产力最高，初级生产力平均水平为1342.802mgC/m~2d；4月次之，初级生产力平均水平为1157.993mgC/m~2d；10月第三，为1092.379mgC/m~2d；冬季最小，12月和2月，研究海区初级生产力仅为873.609mgC/m~2d和786.622mgC/m~2d。
此外，本文讨论了离水辐射亮度的空间分布特征，水-气界面处辐射能量透射系数的空间分布特征，以及入射光和观测几何的变化对离水辐射亮度和水-气界面处辐射能量的透射系数的影响情况。结果表明，恰水面之下水体下行、上行辐照度是一个与入射太阳天顶角有关的变量，随着入射太阳天顶角的增加，恰水面之下下行辐照度和上行辐照度明显衰减。离水辐射亮度的空间分布特征与入射光太阳天顶角有关，离水辐射亮度与观测天顶角之间呈现先增后减的趋势，这种趋势可以用一元三次方程表达。入射光太阳天顶角对透射系数不产生影响，观测方位角和表面粗糙度亦不影响透射系数，但透射系数随着观测天顶角的变化而呈现出明显的衰减特征。透射系数的衰减与观测天顶角可用线性方程来表达。
选择合适的数学方法，利用遥感手段，能够较好的反演二类水体叶绿素浓度及其他水色要素信息。结合遥感数据大空间尺度和长时间尺度的特点，实现对整个研究海区水色要素时空变化分析。VGPM模型可以用来估算研究海区初级生产力，使用根据特定研究区域建立的叶绿素浓度反演算法，提高叶绿素浓度估算精度；采用光衰减系数计算真光层深度，在一定程度上降低了初级生产力的估算误差，避免了VGPM模型对二类水体的不适用性问题。这些研究为开展利用遥感手段进行海洋光学调查和海洋生物地球化学调查和研究提供了参考。
Color remote sensing data has a large horizontal scale and near-synchronizationinstantaneous characteristics, it can monitor the regional distribution and dynamic changes of theocean color elements effetively. Inversed chlorophyll concentration using satellite data is aneffective way to assess the pollution of the marine environment, especially to predict and detectthe red tide, and so on. Using ocean remote sensing data to estimate the ocean carbon storage ishelpful to understand the marine biogeochemical carbon cycle and then the global carbon cycleand climate change, which is significant to study marine ecology and global carbon cycle.
In this study, the mathematical methods including statistical analysis and neural network areused to establish the chlorophyll concentration inversion model for the Yellow sea case II watersusing MODIS images and measured spectral data. The results show that the nonlinear model isslightly better than the linear model; neural network model is superior to the statistical model;the best method for chlorophyll concentration estimation is the Erf-BP neural network model thatis an improved algorithm of traditional back propagation neural network, this work provides atheoretical basis for the chlorophyll concentration estimation and data analysis in-depth.
Remote sensing model for chlorophyll concentration estiamtion is applied to themulti-temporal remote sensing data to gets the different temporal chlorophyll concentrationdistribution in order to analyse the spatial and temporal variation of the Yellow sea chlorophyllconcentration. The statistical results derived from the each month chlorophyll concentrationmaps of2010year show that the minimum, maximum and mean chlorophyll concentration ofeach month is0.320mg/m~3,23.696mg/m~3and3.371mg/m~3respectively. The chlorophyllconcentration is lower in the center areas than that in the coastal areas, and is lower in thenorthern yellow sea than that in the southern areas. This result is consistent with the previousstudies. The spatial and temporal variations of the chlorophyll in different periods were discussedby visual interpretation. It is found that the chlorophyll concentration level of the whole studyarea is higher in spring and early summer due the nutrient control and has a slightly reduce insummer and an increasing trend in autumn; in winter, because of the effect of the temperature,the chlorophyll concentration is lower than other seasons.The chlorophyll concentration isdivided into nine levels according to the statistical characteristics of the chlorophyllconcentration maps, and its temporal and spatial variation is analysed using the gravity centermethod. The gravity center positions of the multi-temporal chlorophyll concentration on eachlevel were calculated and ploted to study the chlorophyll temporal and spatial variationcharacteristics in study area.
Combined with the features of the study area and characteristics of the data, the VGPM(Vertically Generalized Production Model, VGPM) model is selected to estimate the ocean primary productivity, MODIS temperature data, MODIS Kd490(diffuse attenuation coefficientsof down-welling irradiance at490nm) data, SeaWIFS PAR (Photosynthetically Active Radiation,PAR) data and inversed chlorophyll concentration data were used as input parameters for theVGPM model to calculate the primary productivity. And then its seasonal variationcharacteristics were analysed.
The results show that the seasonal variation of primary productivity in the yellow sea isobvious with a bimodal characteristics, which present a trend of spring> autumn> summer>winter. Contrasting the primary productivity in February2010, April, June, August, October andDecember, the maximum value is in June with the value of1342.802mgC/m~2d, followed byApril with the value of1157.993mgC/m~2d, October with the value of1092.379mgC/m~2d, theminimum primary productivity presents in winter with the value of873.609mgC/m~2d and786.622mgC/m~2d in December and February, respectively.
In addition, the spatial distribution characteristics of the water-leaving radiance and thetransmission coefficient of the upward radiance just below the surface, as well as effects of roughsurface, incident light and observation geometry on these distribution characteristics werediscussed. The results shows that downward and upward irradiances just below the surfacedecreased by the increases of solar zenith angle, with a linear relations to the cosine value ofsolar zenith angle. Spatial distribution characteristic of the water-leaving radiance is obvious andhas nothing to do with wind-driven rough surface. A simple cubic equation was developed hereto fit the changes of water-leaving radiance with viewing zenith angle. On the flat surface, thetransmission coefficient of the upward radiance just below the surface does not change with theviewing azimuth angle, however, reduces by the viewing zenith angle increasing and is linearto its square tangent value. On the rough surface, the transmission coefficient of the upwardradiance just below the surface changes little with the viewing azimuth angle but obviously withthe viewing zenith angle. The relationship also exists between transmission coefficient and thesquare tangent value of viewing zenith angle.
Choosing suitable mathematical methods, it is better to retrieve chlorophyll concentrationand other ocean color information of Case II waters based on remote sensing skills. Combinedwith the characteristics of large spatial scales and long temporal scales of remote sensing data, itcan achieve the temporal and spatial variation of ocean color elements in study area. The VGPMmodel can be used to estimate the primary productivity, using the specific chlorophyllconcentration retrieval algorithm based on a specific study area to reduce the estimation error ofthe chlorophyll concentration. Light attenuation coefficient was used to calculate the euphoticdepth, which can some extent reduce the estimation error of the primary productivity and avoidthe limitation of applicability of VGPM model in Case II waters. These studies provide a reference for carrying out the marine optical survey and marine biogeochemical investigationand study by remote sensing tools.
引文
[1] VITOUSEK P M, MOONEY H A, LUBCHENCO J, et al. Human domination of Earth'secosystems [J]. Science,1997,277(5325):494-499.
[2]曲建升,孙成权,张志强, et al.全球变化科学中的碳循环研究进展与趋向[J].地球科学进展,2003,18(006):980-987.
[3] KEELING R F, PIPER S C, BOLLENBACHER A F, et al. Atmospheric CO2records fromsites in the SIO air sampling network. In Trends: ACompendium of Data on Global Change.[J].Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department ofEnergy, Oak Ridge, Tenn, USA,2009,
[4] IPCC. Climate Change2001:The Scientific Basis [M]. Cambridge; Cambridge UniversityPress.2001.
[5] IPCC. Climate Change2007: The Physical Science Basis [M]. Cambridge; CambridgeUniversity Press.2007.
[6]方精云,朴世龙,赵淑清. CO2失汇与北半球中高纬度陆地生态系统的碳汇[J].植物生态学报,2001,25(5):594-602.
[7]徐小锋,宋长春.全球碳循环研究中“碳失汇”研究进展[J].中国科学院研究生院学报,2004,02):145-152.
[8] SCHINDLER D W. Carbon cycling-The mysterious missing sink [J]. Nature,1999,398(6723):105-107.
[9]王效科,白艳莹,欧阳志云, et al.全球碳循环中的失汇及其形成原因[J].生态学报,2002,22(1):94-103.
[10] YUAN D X. The carbon cycle in karst [J]. Z Geomorph N F,1997,108:91-102.
[11]REEBURGH W S. Figures Summarizing the Global Cycles of Biogeochemically ImportantElements [J]. Bulletin of the Ecological Society ofAmerica,1997,78(4):260-267
[12]谭娟,沈新勇,李清泉.海洋碳循环与全球气候变化相互反馈的研究进展[J].气象研究与应用,2009,(1):33-36.
[13]宋金明,徐永福,胡维平, et al.中国近海与湖泊碳的生物地球化学[M].北京:科学出版社,2008.
[14]沈国英,施并章.海洋生态学[M].北京:科学出版社,2002.
[15]LALLI C M, PARSONS T R. Biological Oceanography: An Introduction (2nd Ed.)[M].Oxford England; Butterworth Heinemann.1997.
[16]高爽.北黄海叶绿素和初级生产力的时空变化特征及其影响因素[D];中国海洋大学,2009.
[17]王作华.黄东海叶绿素a和溶解有机碳分布特征及固碳强度初探[D];中国海洋大学,2008.
[18]邹景忠,董丽萍,秦保平.渤海湾富营养化和赤潮问题的初步探讨[J].海洋环境科学,1983,2(2):41-45.
[19]冯春晶.基于人工神经网络的海中叶绿素浓度垂直分布特征研究[D];中国海洋大学,2004.
[20]李宝华,傅克忖,荒川久辛.南黄海叶绿素a与初级生产力之间的相关分析[J].黄渤海海洋学报,1998,16(2):48-53
[21]丛丕福.海洋叶绿素遥感反演及海洋初级生产力估算研究[D];中国科学院研究生院（遥感应用研究所）,2006.
[22]修鹏.渤海海域水色遥感的研究[D];中国海洋大学,2008.
[23]李刚.光学技术在赤潮监测中的应用[D];天津大学,2007.
[24]陈立娣,黄韦艮,陆嘉, et al.利用海上测量光谱进行赤潮监测[J].自然灾害学报,2007,16(2):60-63.
[25]YENTSCH C S. The influence of phytoplankton pigments on the colour of sea water [J].Deep Sea Research (1953),1960,7(1):1-9.
[26]CLARKE G L, EWING G C, LORENZEN C J. Spectra of Backscattered Light from the SeaObtained from Aircraft as a Measure of Chlorophyll Concentration [J]. Science,1970,167(3921):1119-1121.
[27]张海波.利用POIDER数据反演叶绿素a浓度探讨[D];电子科技大学,2007.
[28]KATTAWAR G W, HUMPHREYS T J. THEORETICAL STUDY OF REMOTE SENSINGOF CHLOROPHYLL IN AN ATMOSPHERE-OCEAN ENVIRONMENT [J]. Journal of theOptical Society ofAmerica,1973,63(4):476-476.
[29]KATTAWAR G W, HUMPHREYS T J. REMOTE-SENSING OF CHLOROPHYLL IN ANATMOSPHERE-OCEAN ENVIRONMENT-THEORETICAL-STUDY [J]. Applied Optics,1976,15(1):273-282.
[30]GORDON H R, CLARK D K, BROWN J W, et al. Phytoplankton pigment concentrations inthe Middle Atlantic Bight: comparison of ship determinations and CZCS estimates [J]. AppliedOptics,1983,22(1):20-36.
[31]GORDON H R, MOREL A Y. Remote assessment of ocean color for interpretation ofsatellite visible imagery: A review [J]. Lecture Notes on Coastal and Estuarine Studies,1983,4:118.
[32]LONGHURST A. Interpreting CZCS images of the Amazon plume: Reply to comments by F.E. Muller-Karger, P. L. Richardson and D. McGillicuddy [J]. Deep Sea Research(Part I,Oceanographic Research Papers),1995,42(11):2139-2141.
[33]BRICAUD A, MOREL A, ANDRE J. Spatial/temporal variability of algal biomass andpotential productivity in the Mauritanian upwelling zone, as estimated from CZCS data [J].Advances in Space Research,1987,7(2):53-62.
[34]CLARK D K. Phytoplankton pigment algorithms for the Nimbus-7CZCS(Coastal ZoneColor Scanner)[J]. Oceanography from space,1981,227-237.
[35]M LLER-KARGER F, MCCLAIN C, SAMBROTTO R, et al. A comparison of ship andcoastal zone color scanner mapped distribution of phytoplankton in the southeastern Bering Sea[J]. Journal of Geophysical Research,1990,95(C7):11483-11499.
[36]AIKEN J, F. M J, TREES C C, et al. The SeaWiFS CZCS type pigment algorithm [J]. NASATechnical Memorandum:SeaWiFS Technical Report Serie,1995,104566(29):1-38.
[37]O'REILLY J E, MARITORENA S, MITCHELL B G, et al. Ocean color chlorophyllalgorithms for SeaWiFS [J]. Journal of Geophysical Research-Oceans,1998,103(C11):24937-24953.
[38]MOREL A. Optical modeling of the upper ocean in relation to its biogenous mattercontent(case I waters)[J]. Journal of Geophysical Research,1988,93(C9):10749-10768.
[39]MITCHELL B G, KAHRU M. Algorithms for SeaWiFS standard products developed withthe CalCOFI bio-optical data set [J]. CALCOFI reports,1998,39:133-147.
[40]HIRAWAKE T, SATOH H, MORINAGA T, et al. In-water algorithms for estimation ofchlorophyll a and primary production in the Arabian Sea and the eastern Indian Ocean [M].Ocean Optics XIII. International Society for Optics and Photonics.1997:296-301.
[41]ESAIAS W E, ABBOTT M R, BARTON I, et al. An overview of MODIS capabilities forocean science observations [J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36(4):1250-1265.
[42]GITELSON A, KARNIELI A, GOLDMAN N, et al. Chlorophyll estimation in theSoutheastern Mediterranean using CZCS images: Adaptation of an algorithm and its validation[J]. Journal of Marine Systems,1996,9(3-4):283-290.
[43]ARENZ R F, LEWIS W M, SAUNDERS J F. Determination of chlorophyll and dissolvedorganic carbon from reflectance data for Colorado reservoirs [J]. International Journal of RemoteSensing,1996,17(8):1547-1566.
[44]BRICAUD A, BABIN M, MOREL A, et al. VARIABILITY IN THECHLOROPHYLL-SPECIFIC ABSORPTION-COEFFICIENTS OF NATURALPHYTOPLANKTON-ANALYSIS AND PARAMETERIZATION [J]. Journal of GeophysicalResearch-Oceans,1995,100(C7):13321-13332.
[45]BRICAUD A, MOREL A, BABIN M, et al. Variations of light absorption by suspendedparticles with chlorophyll a concentration in oceanic (case1) waters: Analysis and implicationsfor bio-optical models [J]. Journal of Geophysical Research-Oceans,1998,103(C13):31033-44.
[46]GORDON H R, BROWN O B, EVANS R H, et al. A semianalytic radiance model of oceancolor [J]. Journal of Geophysical Research,1988,93(D9):10909-10924.
[47]BRICAUD A, BABIN M, MOREL A, et al. Variability in the chlorophyll-specific absorptioncoefficients of natural phytoplankton: Analysis and parameterization [J]. JOURNAL OFGEOPHYSICAL RESEARCH-ALL SERIES-,1995,100(C7):13321-13332.
[48]MOREL A, PRIEUR L. Analysis of variations in ocean color [J]. Limnology andOceanography,1977,709-722.
[49]CARDER K L, CHEN F, LEE Z, et al. Semianalytic Moderate-Resolution ImagingSpectrometer algorithms for chlorophyll a and absorption with bio-optical domains based onnitrate-depletion temperatures [J]. Journal of Geophysical Research-Oceans,1999,104(C3):5403-5421.
[50]GARVER S A, SIEGEL D A. Inherent optical property inversion of ocean color spectra andits biogeochemical interpretation1. Time series from the Sargasso Sea [J]. Journal ofGeophysical Research,1997,102(C8):18607-18625.
[51]GORDON H R, MOREL A Y. Remote assessment of ocean color for interpretation ofsatellite visible imagery: Areview [M]. American Geophysical Union,1983.
[52]SIEGEL D A, MICHAELS AF. Quantification of non-algal light attenuation in the SargassoSea: Implications for biogeochemistry and remote sensing [J]. Deep Sea Research Part II:Topical Studies in Oceanography,1996,43(2):321-345.
[53]CARDER K L, STEWARD R G, HARVEY G R, et al. Marine humic and fulvic acids: Theireffects on remote sensing of ocean chlorophyll [J]. Limnology and Oceanography,1989,68-81.
[54]MARITORENA S, SIEGEL D A, PETERSON A R. Optimization of a semianalytical oceancolor model for global-scale applications [J]. Applied Optics,2002,41(15):2705-2714.
[55]HOGE F E, LYON P E. Satellite retrieval of inherent optical properties by linear matrixinversion of oceanic radiance models: an analysis of model and radiance measurement errors [J].Journal of Geophysical Research,1996,101(C7):16631-16648.
[56]O'REILLY J, MARITORENA S, MITCHELL B, et al. Ocean color chlorophyll algorithmsfor SeaWiFS [J]. Journal of Geophysical Research,1998,103(C11):24937-24953.
[57]DEVRED E, FUENTES-YACO C, SATHYENDRANATH S, et al. Asemi-analytic seasonalalgorithm to retrieve chlorophyll-a concentration in the Northwest Atlantic Ocean from SeaWiFSdata [J]. Indian Journal of Marine Sciences,2005,34(4):356-367.
[58]MOREL A, MARITORENA S. Bio-optical properties of oceanic waters: A reappraisal [J].Journal of Geophysical Research-Oceans,2001,106(C4):7163-7180.
[59]ESTEP L, ARNONE R. EFFECT OF WHITECAPS ON DETERMINATION OFCHLOROPHYLL CONCENTRATION FROM SATELLITE DATA [J]. Remote Sensing ofEnvironment,1994,50(3):328-334.
[60]LOISEL H, MOREL A. Light scattering and chlorophyll concentration in case1waters: Areexamination [J]. Limnology and Oceanography,1998,43(5):847-858.
[61]陈淼.海洋水色卫星遥感算法综述[D];中国海洋大学,2005.
[62]张加晋.近岸Ⅱ类水体叶绿素浓度遥感反演的算法综述[J].福建水产,2009,3(1):43-47.
[63]DOERFFER R, FISCHER J. CONCENTRATIONS OF CHLOROPHYLL, SUSPENDEDMATTER, AND GELBSTOFF IN CASE-II WATERS DERIVED FROM SATELLITECOASTAL ZONE COLOR SCANNER DATA WITH INVERSE MODELING METHODS [J].Journal of Geophysical Research-Oceans,1994,99(C4):7457-7466.
[64]XU J, LI F, ZHANG B, et al. Estimation of chlorophyll-a concentration using field spectraldata: a case study in inland Case-II waters, North China [J]. Environmental Monitoring andAssessment,2009,158(1-4):105-116.
[65]OYAMA Y, MATSUSHITA B, FUKUSHIMA T, et al. A new algorithm for estimatingchlorophyll-a concentration from multi-spectral satellite data in case II waters: a simulationbased on a controlled laboratory experiment [J]. International Journal of Remote Sensing,2007,28(7-8):1437-1453.
[66]MOSES W J, GITELSON A A, BERDNIKOV S, et al. Estimation of chlorophyll-aconcentration in case II waters using MODIS and MERIS data-successes and challenges [J].Environmental Research Letters,2009,4(4):045005-045013.
[67]EKSTRAND S. LANDSAT TM BASED QUANTIFICATION OF CHLOROPHYLL-ADURING ALGAE BLOOMS IN COASTAL WATERS [J]. International Journal of RemoteSensing,1992,13(10):1913-1926.
[68]RUDDICK K G, GONS H J, RIJKEBOER M, et al. Optical remote sensing of chlorophyll ain case2waters by use of an adaptive two-band algorithm with optimal error properties [J].Applied Optics,2001,40(21):3575-3585.
[69]GITELSON A A, SCHALLES J F, HLADIK C M. Remote chlorophyll-a retrieval in turbid,productive estuaries: Chesapeake Bay case study [J]. Remote Sensing of Environment,2007,109(4):464-472.
[70]D'SA E J, HU C, MULLER-KARGER F E, et al. Estimation of colored dissolved organicmatter and salinity fields in case2waters using SeaWiFS: Examples from Florida Bay andFlorida Shelf [J]. Proceedings of the Indian Academy of Sciences-Earth and Planetary Sciences,2002,111(3):197-207.
[71]HOOGENBOOM H J, DEKKER A G. The sensitivity of medium resolution imagingspectrometer (MERIS) for detecting chlorophyll and seston dry weight in coastal and inlandwaters; proceedings of the Igarss '98-1998International Geoscience and Remote SensingSymposium, Proceedings Vols1-5: Sensing and Managing the Environment, F,1998[C].
[72]HE M X, LIU Z S, DU K P, et al. Retrieval of chlorophyll from remote-sensing reflectancein the China seas [J]. Applied Optics,2000,39(15):2467-2474.
[73]MAGNUSON A, HARDING L W, MALLONEE M E, et al. Bio-optical model forChesapeake Bay and the Middle Atlantic Bight [J]. Estuarine Coastal and Shelf Science,2004,61(3):403-424.
[74]GORDON H R, BROWN O B, JACOBS M M. Computed relationships between theinherent and apparent optical properties of a flat homogeneous ocean [J]. Applied Optics,1975,14(2):417-427.
[75]LIU C C, MILLER R L. Spectrum matching method for estimating the chlorophyll-aconcentration, CDOM ratio, and backscatter fraction from remote sensing of ocean color [J].Canadian Journal of Remote Sensing,2008,34(4):343-355.
[76]ZHANG M, TANG J, DONG Q, et al. Retrieval of total suspended matter concentration inthe Yellow and East China Seas from MODIS imagery [J]. Remote Sensing of Environment,2010,114(2):392-403.
[77]KEINER L E, YAN X H. Aneural network model for estimating sea surface chlorophyll andsediments from thematic mapper imagery [J]. Remote Sensing of Environment,1998,66(2):153-165.
[78]TANAKA A, KISHINO M, OISHI T, et al. Application of neural network method to case IIwater; proceedings of the Remote Sensing of the Ocean and Sea Ice2000, F,2000[C].
[79]TANAKA A, KISHINO M, DOERFFER R, et al. Development of a neural networkalgorithm for retrieving concentrations of chlorophyll, suspended matter and yellow substancefrom radiance data of the ocean color and temperature scanner [J]. Journal of Oceanography,2004,60(3):519-530.
[80]ZHAN H, SHI P, CHEN C, et al. A Genetic Algorithm for Retrieval of Water Constituentsfrom Ocean Color Remote Sensed Data in Case2Waters [J]. Journal of Remote Sensing,2004,8(1):31-36.
[81]WOZNIAK S B, STRAMSKI D. Modeling the optical properties of mineral particlessuspended in seawater and their influence on ocean reflectance and chlorophyll estimation fromremote sensing algorithms [J]. Applied Optics,2004,43(17):3489-3503.
[82]YAN B, STAMNES K, TORATANI M, et al. Evaluation of a reflectance model used in theSeaWiFS ocean color algorithm: implications for chlorophyll concentration retrievals [J].Applied Optics,2002,41(30):6243-6259.
[83]DARECKI M, WEEKS A, SAGAN S, et al. Optical characteristics of two contrasting Case2waters and their influence on remote sensing algorithms [J]. Continental Shelf Research,2003,23(3-4):237-250.
[84]ANDRE J M, MORELA. ATMOSPHERIC CORRECTIONS AND INTERPRETATION OFMARINE RADIANCES IN CZCS IMAGERY, REVISITED [J]. Oceanologica Acta,1991,14(1):3-22.
[85]CHOMKO R M, GORDON H R, MARITORENAS, et al. Simultaneous retrieval of oceanicand atmospheric parameters for ocean color imagery by spectral optimization: a validation [J].Remote Sensing of Environment,2003,84(2):208-220.
[86]SCHROEDER T, SCHAALE M, FISCHER J. Retrieval of atmospheric and oceanicproperties from MERIS measurements: A new Case-2water processor for BEAM [J].International Journal of Remote Sensing,2007,28(24):5627-5632.
[87]ZHU J, CHEN J, MATSUSHITAB, et al. Atmospheric correction of ENVISAT/MERIS dataover case II waters: the use of black pixel assumption in oxygen and water vapour absorptionbands [J]. International Journal of Remote Sensing,2012,33(12):3713-3732.
[88]BERTHON J F, MORELA. VALIDATION OF ASPECTRALLIGHT-PHOTOSYNTHESISMODEL AND USE OF THE MODEL IN CONJUNCTION WITH REMOTELY SENSEDPIGMENT OBSERVATIONS [J]. Limnology and Oceanography,1992,37(4):781-796.
[89]杨煜,李云梅,王桥, et al.富营养化的太湖水体叶绿素a浓度模型反演[J].地球信息科学学报,2009,05):5597-5603.
[90]任春涛.基于遥感监测的湖泊富营养化状态的模糊模式识别研究[D];内蒙古农业大学,2007.
[91]潘德炉, J.F.G.GOWER,林寿仁.荧光法遥感海面叶绿素浓度的波段选择研究[J].海洋与湖沼,1989,06):564-570.
[92]潘德炉,林寿仁,高尔J F R, et al.利用荧光高度遥感海洋中叶绿素a的浓度[J].海洋学报(中文版),1989,11(06):780-787.
[93]潘德炉,王迪峰.我国海洋光学遥感应用科学研究的新进展[J].地球科学进展,2004,19(04):506-512.
[94]李铜基,陈清莲,朱建华.我国近岸II类海水光谱特性的测量与研究[J].海洋通报,2002,21(06):9-15.
[95]汪小勇,李铜基,唐军武, et al.二类水体表观光学特性的测量与分析——水面之上法方法研究[J].海洋技术,2004,23(02):1-6+23.
[96]唐军武,田国良.水色光谱分析与多成分反演算法[J].遥感学报,1997,1(04):252-256.
[97]刘堂友,匡定波,尹球.湖泊藻类叶绿素-a和悬浮物浓度的高光谱定量遥感模型研究[J].红外与毫米波学报,2004,23(01):11-15.
[98]黄家洁.基于MODIS数据的二类水体叶绿素反演算法理论及其实现[M].第十五届全国遥感技术学术交流会.中国贵阳.2005.
[99]万幼川,黄家洁,刘良明.基于MODIS数据的二类水体叶绿素反演算法理论与实现[J].武汉大学学报(信息科学版),2007,32(07):572-575.
[100]马超飞,蒋兴伟,唐军武, et al. HY-1CCD宽波段水色要素反演算法[J].海洋学报(中文版),2005,27(04):38-44.
[101]蒋刚,肖建,郑永康, et al.基于统计学习理论的海色遥感叶绿素浓度反演方法[J].中国海洋大学学报(自然科学版),2006,36(04):559-563.
[102]蒋刚,肖建,郑永康, et al.基于支持向量机的一类水域叶绿素a浓度反演研究[J].计算机应用,2005,25(10):2398-2400+2409.
[103]徐京萍,张柏,宋开山, et al.基于半分析模型的新庙泡叶绿素a浓度反演研究[J].红外与毫米波学报,2008,27(3):197-201.
[104]徐京萍,张柏,段洪涛, et al.新庙泡叶绿素a浓度高光谱定量模型研究[J].遥感技术与应用,2007,22(04):497-502.
[105]于堃,陆殿梅,熊黑钢.近7年来渤海海区冬季表层海水叶绿素浓度的遥感反演及其变化分析[J].遥感信息,2009,(06):55-62.
[106]曾炼.直接反演式水色遥感算法研究[D];华中科技大学,2009.
[107]刘斌.一类水域中从海面反射比反演叶绿素浓度垂直分布初探[D];中国海洋大学,2009.
[108]张彦喆,郑小慎,张波.渤海海域叶绿素浓度的遥感反演研究[J].天津科技大学学报,2010,25(01):51-53.
[109] NIELSEN E S. The Use of Radio-active Carbon (C14) for Measuring OrganicProduction in the Sea [J]. J Cons int Explor Mer,1952,18(2):117-140.
[110] RYTHER J H, YENTSCH C S. The estimation of phytoplankton production in theocean from chlorophyll and light data [J]. Limnology and Oceanography,1957,281-6.
[111] TALLING J. The phytoplankton population as a compound photosynthetic system [J].New phytologist,1957,56(2):133-149.
[112] CAD E G, HEGEMAN J. Primary production of phytoplankton in the Dutch WaddenSea [J]. Netherlands Journal of Sea Research,1974,8(2-3):240-259.
[113] BEHRENFELD M J, FALKOWSKI P G. A consumer's guide to phytoplankton primaryproductivity models [J]. Limnology and Oceanography,1997,42(7):1479-1491.
[114] SATHYENDRANATH S, PLATT T. Computation of aquatic primary production:extended formalism to include effect of angular and spectral distribution of light [J]. Limnologyand Oceanography,1989,34(01):188-198.
[115] SAKSHAUG E, ANDRESEN K, KIEFER D A. A steady state description of growthand light absorption in the marine planktonic diatom Skeletonema costatum [J]. Limnology andOceanography,1989,34(01):198-205.
[116] MOREL A. Available, usable, and stored radiant energy in relation to marinephotosynthesis [J]. Deep Sea Research,1978,25(8):673-688.
[117] FALKOWSKI P G, WOODHEAD A D. Primary productivity and biogeochemicalcycles in the sea [M]. Springer,1992.
[118] SMITH R C, BAKER K S. The bio-optical state of ocean waters and remote sensing [J].Limnology and Oceanography,1978,23(2):247-259.
[119] EPPLEY R, STEWART E, ABBOTT M R, et al. Estimating ocean primary productionfrom satellite chlorophyll. Introduction to regional differences and statistics for the SouthernCalifornia Bight [J]. Journal of Plankton Research,1985,7(1):57-70.
[120] FALKOWSKI P G. Light-shade adaptation and assimilation numbers [J]. Journal ofPlankton Research,1981,3(2):203-216.
[121] PLATT T, SATHYENDRANATH S. Estimators of primary production for interpretationof remotely sensed data on ocean color [J]. Journal of Geophysical Research: Oceans,1993,98(C8):14561-14576.
[122]唐世林,陈楚群,詹海刚.海洋初级生产力的遥感研究进展[J].台湾海峡,2006,25(4):591-598.
[123] SMITH R C, EPPLEY R W, BAKER K S. Correlation of primary production asmeasured aboard ship in Southern California Coastal waters and as estimated from satellitechlorophyll images [J]. Marine Biology.,1982,66(3):281-288.
[124]郝锵.中国近海叶绿素和初级生产力的时空分布特征和环境调控机制研究[D];中国海洋大学,2010.
[125] PLATT T. Primary production of the ocean water column as a function of surface lightintensity: algorithms for remote sensing [J]. Deep Sea Research,1986,33(2):149-163.
[126] MOREL A. Light and marine photosynthesis: a spectral model with geochemical andclimatological implications [J]. Progress in Oceanography,1991,26(3):263-306.
[127] SATHYENDRANATH S, LONGHURST A, CAVERHILL C M, et al. Regionally andseasonally differentiated primary production in the North Atlantic [J]. Deep Sea Research Part I:Oceanographic Research Papers,1995,42(10):1773-1802.
[128] LONGHURST A, SATHYENDRANATH S, PLATT T, et al. An estimate of globalprimary production in the ocean from satellite radiometer data [J]. Journal of Plankton Research,1995,17(6):1245-1271.
[129] ANTOINE D, ANDRT J M, MOREL A. Oceanic primary production2. Estimation atglobal scale from satellite (coastal zone color scanner) chlorophyll [J]. Global BiogeochemicalCycles,1996,10(1):57-69.
[130] BEHRENFELD M J, FALKOWSKI P G. Photosynthetic rates derived fromsatellite-based chlorophyll concentration [J]. Limnology and Oceanography,1997,42(1):1-20.
[131] WOZNIAK B, DERA J, FICEK D, et al. Modelling light and photosynthesis in themarine environment [J]. Oceanologia,2003,45(2):171-245.
[132] SMYTH T, TILSTONE G, GROOM S. Integration of radiative transfer into satellitemodels of ocean primary production [J]. Journal of Geophysical Research,2005,110(C10):C10014.
[133] BALCH W, EVANS R, BROWN J, et al. THE REMOTE-SENSING OF OCEANPRIMARY PRODUCTIVITY-USE OF A NEW DATA COMPILATION TO TESTSATELLITE ALGORITHMS [J]. Journal of Geophysical Research-Oceans,1992,97(C2):2279-2293.
[134] PLATT T, SATHYENDRANATH S. Oceanic primary production: estimation by remotesensing at local and regional scales [J]. Science,1988,241(4873):1613-1620.
[135] MUELLER J L, LANGE R E. Bio-optical provinces of the northeast Pacific Ocean: aprovisional analysis [J]. Limnology and Oceanography,1989,34(8):1572-1586.
[136]李国胜,邵宇宾.海洋初级生产力遥感与GIS评估模型研究[J].地理学报,1998,53(06):546-552
[137]官文江,何贤强,潘德炉, et al.渤、黄、东海海洋初级生产力的遥感估算[J].水产学报,2005,03):367-72.
[138]檀赛春,石广玉.中国近海初级生产力的遥感研究及其时空演化[J].地理学报,2006,61(11):1189-1199.
[139]曾呈奎,徐鸿儒,王春林.中国海洋志[M].郑州:大象出版社,2003.
[140]王昊寅.南黄海泥质区全新世底栖有孔虫及其环境意义[D];中国海洋大学,2012.
[141]国家海洋局908专项办公室.海洋生物生态调查技术规程[M].北京:海洋出版社,2006.
[142]樊辉.黄河口泥沙输移及三角洲的近期演变[D];中国科学院研究生院(海洋研究所),2005.
[143]樊辉,黄海军.南黄海辐射沙洲邻近海域表层悬浮颗粒物浓度遥感反演[J].地理科学,2011,31(02):159-165.
[144]唐军武,田国良,汪小勇, et al.水体光谱测量与分析Ⅰ:水面以上测量法[J].遥感学报,2004,8(01):37-44.
[145]王繁,周斌,徐建明, et al.杭州湾混浊水体表面光谱测量及光谱特征分析[J].光谱学与光谱分析,2009,29(3):730-734.
[146]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
[147]陈瑾,麻金继,王家成.海岸带混浊水域MODIS图像的大气校正[J].大气与环境光学学报,2007,2(4):306-311.
[148]陈军,孙记红,付军.基于分区暗像元和Spline插值方法估算太湖气溶胶光学厚度[J].遥感信息,2011,(03):33-37.
[149] PAN D, MAO Z. Atmospheric correction for China's coastal water colorremote sensing[J]. Acta Oceanologica Sinica,2001,20(03):343-354.
[150] GORDON H R, CASTA O D J. Aerosol analysis with the Coastal Zone Color Scanner:a simple method for including multiple scattering effects [J]. Applied Optics,1989,28(7):1320-1326.
[151] RUDOLF R. Afast atmospheric correction algorithm applied to Landsat TM images [J].International Journal of Remote Sensing1990,11(1):159-166.
[152]焦红波,查勇,李云梅, et al.基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型[J].遥感学报,2006,10(002):242-248.
[153]荀尚培,翟武全,范伟. MODIS巢湖水体叶绿素a浓度反演模型[J].应用气象学报,2009,20(1):95-101.
[154] JOINT I, GROOM S B. Estimation of phytoplankton production from space: currentstatus and future potential of satellite remote sensing [J]. Journal of Experimental MarineBiology and Ecology,2000,250(1):233-255.
[155]段洪涛,张柏,宋开山, et al.查干湖叶绿素a浓度高光谱定量模型研究[J].环境科学,2006,27(3):503-507.
[156] SATHYENDRANATH S, PRIEUR L, MOREL A. A three-component model of oceancolour and its application to remote sensing of phytoplankton pigments in coastal waters [J].International Journal of Remote Sensing,1989,10(8):1373-1394.
[157] GORDON H R. DEPENDENCE OF THE DIFFUSE REFLECTANCE OFNATURAL-WATERS ON THE SUN ANGLE [J]. Limnology and Oceanography,1989,34(8):1484-1489.
[158] MOREL A, GENTILI B. DIFFUSE-REFLECTANCE OF OCEANIC WATERS.2.BIDIRECTIONALASPECTS [J].Applied Optics,1993,32(33):6864-7689.
[159] MOREL A, VOSS K J, GENTILI B. BIDIRECTIONAL REFLECTANCE OFOCEANIC WATERS-A COMPARISON OF MODELED AND MEASURED UPWARDRADIANCE FIELDS [J]. Journal of Geophysical Research-Oceans,1995,100(C7):13143-13150.
[160] MOREL A, GENTILI B. DIFFUSE REFLECTANCE OF OCEANIC WATERS-ITSDEPENDENCE ON SUN ANGLE AS INFLUENCED BY THE MOLECULAR-SCATTERINGCONTRIBUTION [J].Applied Optics,1991,30(30):4427-4438.
[161] MARITORENA S, MOREL A, GENTILI B. DIFFUSE-REFLECTANCE OFOCEANIC SHALLOW WATERS-INFLUENCE OF WATER DEPTH AND BOTTOMALBEDO [J]. Limnology and Oceanography,1994,39(7):1689-1703.
[162] JIN Z H, CHARLOCK T P, RUTLEDGE K, et al. Analytical solution of radiativetransfer in the coupled atmosphere-ocean system with a rough surface [J]. Applied Optics,2006,45(28):7443-7455.
[163]唐军武,田国良,陈清莲.离水辐射非朗伯特性的Monte Carlo模拟及分析[J].海洋学报(中文版),2000,22(02):48-57.
[164]唐军武.海洋光学特性模拟与遥感模型[D].北京;中国科学院遥感应用研究所,1999.
[165] LING Z, ZHOU B, JIANG J, et al. Modeling the Bidirectional Reflectance DistributionFunction (BRDF) of water based on Monte Carlo simulation [M]//TONG Q G X Z B. RemoteSensing of the Environment: The17th China Conference on Remote Sensing.2011.
[166]徐希孺.遥感物理[M].北京大学出版社,2005.
[167]刘玉光.卫星海洋学[M].北京:高等教育出版社,2009.
[168] COX C, MUNK W. MEASUREMENT OF THE ROUGHNESS OF THE SEASURFACE FROM PHOTOGRAPHS OF THE SUNS GLITTER [J]. Journal of the OpticalSociety ofAmerica,1954,44(11):838-850.
[169]何晓群.多元统计分析[M].北京:中国人民大学出版社,2004.
[170]徐文科,仲莉娜.概率论与数理统计[M].哈尔滨:东北林业大学出版社,2003.
[171]屈鸿.回复式神经网络及其在组合优化问题中的应用[D];电子科技大学,2006.
[172]刘旭升,李锋,昝国胜, et al.基于GIS和神经网络的森林植被分类(英文)[J].遥感学报,2007,11(05):710-717.
[173]李永亮,林辉,孙华, et al.基于BP神经网络的森林树种分类研究[J].中南林业科技大学学报,2010,30(11):43-46.
[174]李健,麻金继,吉玮.基于广义回归神经网络反演悬浮泥沙含量的定量遥感方法[J].大气与环境光学学报,2010,5(04):305-310.
[175]詹海刚,平施,陈楚群.利用神经网络反演海水叶绿素浓度[J].科学通报,2000,45(17):1879-1884.
[176]李伟.人工神经网络方法反演东中国海悬移质浓度[J].海洋湖沼通报,2007,(04):55-58.
[177]仲京臣,汪小勇,陈清莲.基于神经网络的黄东海春季二类水体三要素浓度反演方法[J].海洋技术,2005,24(04):118-122.
[178]丛丕福,牛铮,曲丽梅, et al.基于神经网络和TM图像的大连湾海域悬浮物质量浓度的反演[J].海洋科学,2005,29(04):31-35.
[179]张亭禄,李肖霞.基于人工神经网络的海水漫射衰减系数的遥感反演方法[J].中国海洋大学学报(自然科学版),2007,37(04):676-680.
[180]曲元.海洋污染水体的卫星遥感监测方法研究[D];大连海事大学,2000.
[181]卢晓亭,孙勇,笪良龙, et al.基于EMD的BP神经网络海水温度时间序列预测研究[J].海洋技术,2009,28(03):79-82.
[182]韩震,赵宁.基于LM-BP神经网络的Argo数据西北太平洋海水温度模型[J].海洋环境科学,2012,31(04):555-560.
[183]孙凌.针对HY-1ACCD的大气修正与水体组分反演[D];中国科学院研究生院（海洋研究所）,2005.
[184]王丽静. AMSR-E卫星海面气象参数遥感方法研究[D];中国科学院研究生院（海洋研究所）,2008.
[185]叶世伟,史忠植.神经网络原理[M].北京:机械工程出版社,2006.
[186]沈花玉,王兆霞,高成耀, et al. BP神经网络隐含层单元数的确定[J].天津理工大学学报,2008,24(05):13-15.
[187]杨曦光.高光谱数据提取森林冠层叶绿素及氮含量的研究[D];东北林业大学,2010.
[188]吴昌友.神经网络的研究及应用[D];东北农业大学,2007.
[189]郭海涛,张殿伦,马国芳, et al.使用BP算法时应考虑的若干问题[J].佳木斯大学学报(自然科学版),2000,18(04):363-365.
[190]夏克文,李昌彪,沈钧毅.前向神经网络隐含层节点数的一种优化算法[J].计算机科学,2005,32(10):143-145.
[191]徐小军.基于LANDSAT TM影像毛竹林地上部分碳储量估算研究[D];浙江林学院,2009.
[192]杨博,王亚东,苏小红.一种基于误差放大的快速BP学习算法[J].计算机研究与发展,2004,41(05):774-779.
[193]夏爱国. BP改进算法研究及一种系统控制训练算法[J].系统仿真学报,1999,10(02):61-4+74.
[194]金丕彦,芮勇. BP算法各种改进算法的研究及应用[J].南京航空航天大学学报,1994,26(S1):201-205.
[195]徐小军,杜华强,周国模, et al. Erf-BP混合像元分解及在森林遥感信息提取中应用[J].林业科学,2011,47(02):30-38.
[196]范文义,张海玉,于颖, et al.三种森林生物量估测模型的比较分析[J].植物生态学报,2011,35(04):402-410.
[197]杨曦光,于颖,黄海军, et al.森林冠层氮含量遥感估算[J].红外与毫米波学报,2012,31(06):536-543.
[198]张江涛,殷克东.黄海春季表层叶绿素和初级生产力及其粒径结构研究[J].生态环境学报,2010,19(09):2107-2111.
[199]郑国侠,宋金明,戴纪翠, et al.南黄海秋季叶绿素a的分布特征与浮游植物的固碳强度[J].海洋学报(中文版),2006,28(03):109-118.
[200]朱明远,毛兴华,吕瑞华, et al.黄海海区的叶绿素a和初级生产力[J].黄渤海海洋,1993,11(03):38-51.
[201]李泽军.从水体叶绿素含量评价于桥水库富营养化程度[J].河北水利水电技术,2001,(06):44-45.
[202]张穗,何报寅.河口Ⅱ类水体富营养化的遥感定量方法研究[J].长江科学院院报,2004,21(03):29-31.
[203]吴传庆,盖嘉翔,王桥, et al.湖泊富营养化遥感评价模型的建立方法[J].中国环境监测,2011,27(05):77-82.
[204]王志辉.基于MODIS的淡水湖泊富营养化状况遥感监测[D];华中科技大学,2007.
[205]苗群.南四湖水环境质量评价研究[D];青岛大学,2008.
[206]牟秀菊,黄连光,于洋, et al.日照市近岸海域水质变化趋势调查及评价[J].河北渔业,2010,(11):35-38.
[207]牛显春,周建敏,林哲晖.茂名近岸海域富营养化检测与评价[J].茂名学院学报,2006,16(06):10-13.
[208]杨建设,张家寿,杨孝军, et al.茂名市近海养殖区水环境质量的检测[J].茂名学院学报,2005,15(01):1-4+29.
[209]王保栋,孙霞,韦钦胜, et al.我国近岸海域富营养化评价新方法及应用[J].海洋学报(中文版),2012,34(04):61-66.
[210]王保栋,战闰,藏家业.黄海、东海浮游植物生长的营养盐限制性因素初探[J].海洋学报(中文版),2003,25(S2):190-195.
[211]高爽,李正炎.北黄海夏、冬季叶绿素和初级生产力的空间分布和季节变化特征[J].中国海洋大学学报(自然科学版),2009,39(04):604-610.
[212] CONG P, QU L, HAN G, et al. Remotely sensed detection and application analysis ofocean particulate organic carbon [J]. Marine Environmental Science,2012,31(2):300-304.
[213] SHANG S, LEE Z, WEI G. Characterization of MODIS-derived euphotic zone depth:Results for the China Sea [J]. Remote Sensing of Environment,2011,115(1):180-186.
[214] STRAMSKA M. Particulate organic carbon in the global ocean derived from SeaWiFSocean color [J]. Deep-Sea Research Part I-Oceanographic Research Papers,2009,56(9):1459-1470.
[215]唐世林,陈楚群,詹海刚.海洋初级生产力的遥感研究进展[J].台湾海峡,2006,25(04):591-598.
[216]邹亚荣,马超飞,邵岩.遥感海洋初级生产力的研究进展[J].遥感信息,2005,(02):58-61.
[217]费尊乐,李宝华.叶绿素a与初级生产力之间的相关关系[J].青岛海洋大学学报,1990,20(01):73-80.
[218]费尊乐, C．C．TREES,李宝华.利用叶绿素资料计算初级生产力[J].黄渤海海洋,1997,15(01):35-47.
[219] PARSONS T R, TAKAHASHI M, HARGRAVE B. Biological oceanographic processes[M]. OXFORD(UK: PERGAMON PRESS,1983.
[220]李国胜,王芳,梁强, et al.东海初级生产力遥感反演及其时空演化机制[J].地理学报,2003,58(04):483-493.
[221]李小斌,陈楚群,施平, et al.南海1998——2002年初级生产力的遥感估算及其时空演化机制[J].热带海洋学报,2006,25(03):57-62.
[222]邵宇宾,李国胜.遥感与地理信息系统在海洋初级生产力研究中的应用[J].地理学报,1995,50(S1):119-125.
[223]殷燕,张运林,时志强, et al.基于VGPM模型和MODIS数据估算梅梁湾浮游植物初级生产力[J].生态学报,2012,32(11):3528-3537.
[224] SISWANTO E, ISHIZAKA J, YOKOUCHI K. Optimal primary production model andparameterization in the eastern East China Sea [J]. Journal of Oceanography,2006,62(03):361-372
[225] LI G, GAO P, WANG F, et al. Estimation of ocean primary productivity and itsspatio-temporal variation mechanism for East China Sea based on VGPM model [J]. Journal ofGeographical Sciences,2004,14(S1):34-42.
[226] ISHIZAKA J, SISWANTO E, ITOH T, et al. Verification of vertically generalizedproduction model and estimation of primary production in Sagami Bay, Japan [J]. Journal ofOceanography,2007,63(03):517-524.
[227] KAMEDA T, ISHIZAKA J. Size-Fractionated Primary Production Estimated by aTwo-Phytoplankton Community Model Applicable to Ocean Color Remote Sensing [J]. Journalof Oceanography,2005,61(04):663-672.
[228] FROUIN R, FRANZ B, WANG M. Algorithm to estimate PAR from SeaWiFS dataVersion1.2-Documentation [J]. NASA GSFC, available at http://oceancolor gsfc nasagov/DOCS/seawifs_par_wfigs pdf,2001,
[229]贾艳红,孙莹,牛博颖.基于RS和GIS的黄海海区海洋初级生产力估算[J].淮海工学院学报(自然科学版),2010,19(04):87-91.
[230] GARCIA-SOTO C, PINGREE R D. Spring and summer blooms of phytoplankton(SeaWiFS/MODIS) along a ferry line in the Bay of Biscay and western English Channel [J].Continental Shelf Research,2009,29(8):1111-1122.
[2]曲建升,孙成权,张志强, et al.全球变化科学中的碳循环研究进展与趋向[J].地球科学进展,2003,18(006):980-987.
[3] KEELING R F, PIPER S C, BOLLENBACHER A F, et al. Atmospheric CO2records fromsites in the SIO air sampling network. In Trends: ACompendium of Data on Global Change.[J].Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department ofEnergy, Oak Ridge, Tenn, USA,2009,
[4] IPCC. Climate Change2001:The Scientific Basis [M]. Cambridge; Cambridge UniversityPress.2001.
[5] IPCC. Climate Change2007: The Physical Science Basis [M]. Cambridge; CambridgeUniversity Press.2007.
[6]方精云,朴世龙,赵淑清. CO2失汇与北半球中高纬度陆地生态系统的碳汇[J].植物生态学报,2001,25(5):594-602.
[7]徐小锋,宋长春.全球碳循环研究中“碳失汇”研究进展[J].中国科学院研究生院学报,2004,02):145-152.
[8] SCHINDLER D W. Carbon cycling-The mysterious missing sink [J]. Nature,1999,398(6723):105-107.
[9]王效科,白艳莹,欧阳志云, et al.全球碳循环中的失汇及其形成原因[J].生态学报,2002,22(1):94-103.
[10] YUAN D X. The carbon cycle in karst [J]. Z Geomorph N F,1997,108:91-102.
[11]REEBURGH W S. Figures Summarizing the Global Cycles of Biogeochemically ImportantElements [J]. Bulletin of the Ecological Society ofAmerica,1997,78(4):260-267
[12]谭娟,沈新勇,李清泉.海洋碳循环与全球气候变化相互反馈的研究进展[J].气象研究与应用,2009,(1):33-36.
[13]宋金明,徐永福,胡维平, et al.中国近海与湖泊碳的生物地球化学[M].北京:科学出版社,2008.
[14]沈国英,施并章.海洋生态学[M].北京:科学出版社,2002.
[15]LALLI C M, PARSONS T R. Biological Oceanography: An Introduction (2nd Ed.)[M].Oxford England; Butterworth Heinemann.1997.
[16]高爽.北黄海叶绿素和初级生产力的时空变化特征及其影响因素[D];中国海洋大学,2009.
[17]王作华.黄东海叶绿素a和溶解有机碳分布特征及固碳强度初探[D];中国海洋大学,2008.
[18]邹景忠,董丽萍,秦保平.渤海湾富营养化和赤潮问题的初步探讨[J].海洋环境科学,1983,2(2):41-45.
[19]冯春晶.基于人工神经网络的海中叶绿素浓度垂直分布特征研究[D];中国海洋大学,2004.
[20]李宝华,傅克忖,荒川久辛.南黄海叶绿素a与初级生产力之间的相关分析[J].黄渤海海洋学报,1998,16(2):48-53
[21]丛丕福.海洋叶绿素遥感反演及海洋初级生产力估算研究[D];中国科学院研究生院（遥感应用研究所）,2006.
[22]修鹏.渤海海域水色遥感的研究[D];中国海洋大学,2008.
[23]李刚.光学技术在赤潮监测中的应用[D];天津大学,2007.
[24]陈立娣,黄韦艮,陆嘉, et al.利用海上测量光谱进行赤潮监测[J].自然灾害学报,2007,16(2):60-63.
[25]YENTSCH C S. The influence of phytoplankton pigments on the colour of sea water [J].Deep Sea Research (1953),1960,7(1):1-9.
[26]CLARKE G L, EWING G C, LORENZEN C J. Spectra of Backscattered Light from the SeaObtained from Aircraft as a Measure of Chlorophyll Concentration [J]. Science,1970,167(3921):1119-1121.
[27]张海波.利用POIDER数据反演叶绿素a浓度探讨[D];电子科技大学,2007.
[28]KATTAWAR G W, HUMPHREYS T J. THEORETICAL STUDY OF REMOTE SENSINGOF CHLOROPHYLL IN AN ATMOSPHERE-OCEAN ENVIRONMENT [J]. Journal of theOptical Society ofAmerica,1973,63(4):476-476.
[29]KATTAWAR G W, HUMPHREYS T J. REMOTE-SENSING OF CHLOROPHYLL IN ANATMOSPHERE-OCEAN ENVIRONMENT-THEORETICAL-STUDY [J]. Applied Optics,1976,15(1):273-282.
[30]GORDON H R, CLARK D K, BROWN J W, et al. Phytoplankton pigment concentrations inthe Middle Atlantic Bight: comparison of ship determinations and CZCS estimates [J]. AppliedOptics,1983,22(1):20-36.
[31]GORDON H R, MOREL A Y. Remote assessment of ocean color for interpretation ofsatellite visible imagery: A review [J]. Lecture Notes on Coastal and Estuarine Studies,1983,4:118.
[32]LONGHURST A. Interpreting CZCS images of the Amazon plume: Reply to comments by F.E. Muller-Karger, P. L. Richardson and D. McGillicuddy [J]. Deep Sea Research(Part I,Oceanographic Research Papers),1995,42(11):2139-2141.
[33]BRICAUD A, MOREL A, ANDRE J. Spatial/temporal variability of algal biomass andpotential productivity in the Mauritanian upwelling zone, as estimated from CZCS data [J].Advances in Space Research,1987,7(2):53-62.
[34]CLARK D K. Phytoplankton pigment algorithms for the Nimbus-7CZCS(Coastal ZoneColor Scanner)[J]. Oceanography from space,1981,227-237.
[35]M LLER-KARGER F, MCCLAIN C, SAMBROTTO R, et al. A comparison of ship andcoastal zone color scanner mapped distribution of phytoplankton in the southeastern Bering Sea[J]. Journal of Geophysical Research,1990,95(C7):11483-11499.
[36]AIKEN J, F. M J, TREES C C, et al. The SeaWiFS CZCS type pigment algorithm [J]. NASATechnical Memorandum:SeaWiFS Technical Report Serie,1995,104566(29):1-38.
[37]O'REILLY J E, MARITORENA S, MITCHELL B G, et al. Ocean color chlorophyllalgorithms for SeaWiFS [J]. Journal of Geophysical Research-Oceans,1998,103(C11):24937-24953.
[38]MOREL A. Optical modeling of the upper ocean in relation to its biogenous mattercontent(case I waters)[J]. Journal of Geophysical Research,1988,93(C9):10749-10768.
[39]MITCHELL B G, KAHRU M. Algorithms for SeaWiFS standard products developed withthe CalCOFI bio-optical data set [J]. CALCOFI reports,1998,39:133-147.
[40]HIRAWAKE T, SATOH H, MORINAGA T, et al. In-water algorithms for estimation ofchlorophyll a and primary production in the Arabian Sea and the eastern Indian Ocean [M].Ocean Optics XIII. International Society for Optics and Photonics.1997:296-301.
[41]ESAIAS W E, ABBOTT M R, BARTON I, et al. An overview of MODIS capabilities forocean science observations [J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36(4):1250-1265.
[42]GITELSON A, KARNIELI A, GOLDMAN N, et al. Chlorophyll estimation in theSoutheastern Mediterranean using CZCS images: Adaptation of an algorithm and its validation[J]. Journal of Marine Systems,1996,9(3-4):283-290.
[43]ARENZ R F, LEWIS W M, SAUNDERS J F. Determination of chlorophyll and dissolvedorganic carbon from reflectance data for Colorado reservoirs [J]. International Journal of RemoteSensing,1996,17(8):1547-1566.
[44]BRICAUD A, BABIN M, MOREL A, et al. VARIABILITY IN THECHLOROPHYLL-SPECIFIC ABSORPTION-COEFFICIENTS OF NATURALPHYTOPLANKTON-ANALYSIS AND PARAMETERIZATION [J]. Journal of GeophysicalResearch-Oceans,1995,100(C7):13321-13332.
[45]BRICAUD A, MOREL A, BABIN M, et al. Variations of light absorption by suspendedparticles with chlorophyll a concentration in oceanic (case1) waters: Analysis and implicationsfor bio-optical models [J]. Journal of Geophysical Research-Oceans,1998,103(C13):31033-44.
[46]GORDON H R, BROWN O B, EVANS R H, et al. A semianalytic radiance model of oceancolor [J]. Journal of Geophysical Research,1988,93(D9):10909-10924.
[47]BRICAUD A, BABIN M, MOREL A, et al. Variability in the chlorophyll-specific absorptioncoefficients of natural phytoplankton: Analysis and parameterization [J]. JOURNAL OFGEOPHYSICAL RESEARCH-ALL SERIES-,1995,100(C7):13321-13332.
[48]MOREL A, PRIEUR L. Analysis of variations in ocean color [J]. Limnology andOceanography,1977,709-722.
[49]CARDER K L, CHEN F, LEE Z, et al. Semianalytic Moderate-Resolution ImagingSpectrometer algorithms for chlorophyll a and absorption with bio-optical domains based onnitrate-depletion temperatures [J]. Journal of Geophysical Research-Oceans,1999,104(C3):5403-5421.
[50]GARVER S A, SIEGEL D A. Inherent optical property inversion of ocean color spectra andits biogeochemical interpretation1. Time series from the Sargasso Sea [J]. Journal ofGeophysical Research,1997,102(C8):18607-18625.
[51]GORDON H R, MOREL A Y. Remote assessment of ocean color for interpretation ofsatellite visible imagery: Areview [M]. American Geophysical Union,1983.
[52]SIEGEL D A, MICHAELS AF. Quantification of non-algal light attenuation in the SargassoSea: Implications for biogeochemistry and remote sensing [J]. Deep Sea Research Part II:Topical Studies in Oceanography,1996,43(2):321-345.
[53]CARDER K L, STEWARD R G, HARVEY G R, et al. Marine humic and fulvic acids: Theireffects on remote sensing of ocean chlorophyll [J]. Limnology and Oceanography,1989,68-81.
[54]MARITORENA S, SIEGEL D A, PETERSON A R. Optimization of a semianalytical oceancolor model for global-scale applications [J]. Applied Optics,2002,41(15):2705-2714.
[55]HOGE F E, LYON P E. Satellite retrieval of inherent optical properties by linear matrixinversion of oceanic radiance models: an analysis of model and radiance measurement errors [J].Journal of Geophysical Research,1996,101(C7):16631-16648.
[56]O'REILLY J, MARITORENA S, MITCHELL B, et al. Ocean color chlorophyll algorithmsfor SeaWiFS [J]. Journal of Geophysical Research,1998,103(C11):24937-24953.
[57]DEVRED E, FUENTES-YACO C, SATHYENDRANATH S, et al. Asemi-analytic seasonalalgorithm to retrieve chlorophyll-a concentration in the Northwest Atlantic Ocean from SeaWiFSdata [J]. Indian Journal of Marine Sciences,2005,34(4):356-367.
[58]MOREL A, MARITORENA S. Bio-optical properties of oceanic waters: A reappraisal [J].Journal of Geophysical Research-Oceans,2001,106(C4):7163-7180.
[59]ESTEP L, ARNONE R. EFFECT OF WHITECAPS ON DETERMINATION OFCHLOROPHYLL CONCENTRATION FROM SATELLITE DATA [J]. Remote Sensing ofEnvironment,1994,50(3):328-334.
[60]LOISEL H, MOREL A. Light scattering and chlorophyll concentration in case1waters: Areexamination [J]. Limnology and Oceanography,1998,43(5):847-858.
[61]陈淼.海洋水色卫星遥感算法综述[D];中国海洋大学,2005.
[62]张加晋.近岸Ⅱ类水体叶绿素浓度遥感反演的算法综述[J].福建水产,2009,3(1):43-47.
[63]DOERFFER R, FISCHER J. CONCENTRATIONS OF CHLOROPHYLL, SUSPENDEDMATTER, AND GELBSTOFF IN CASE-II WATERS DERIVED FROM SATELLITECOASTAL ZONE COLOR SCANNER DATA WITH INVERSE MODELING METHODS [J].Journal of Geophysical Research-Oceans,1994,99(C4):7457-7466.
[64]XU J, LI F, ZHANG B, et al. Estimation of chlorophyll-a concentration using field spectraldata: a case study in inland Case-II waters, North China [J]. Environmental Monitoring andAssessment,2009,158(1-4):105-116.
[65]OYAMA Y, MATSUSHITA B, FUKUSHIMA T, et al. A new algorithm for estimatingchlorophyll-a concentration from multi-spectral satellite data in case II waters: a simulationbased on a controlled laboratory experiment [J]. International Journal of Remote Sensing,2007,28(7-8):1437-1453.
[66]MOSES W J, GITELSON A A, BERDNIKOV S, et al. Estimation of chlorophyll-aconcentration in case II waters using MODIS and MERIS data-successes and challenges [J].Environmental Research Letters,2009,4(4):045005-045013.
[67]EKSTRAND S. LANDSAT TM BASED QUANTIFICATION OF CHLOROPHYLL-ADURING ALGAE BLOOMS IN COASTAL WATERS [J]. International Journal of RemoteSensing,1992,13(10):1913-1926.
[68]RUDDICK K G, GONS H J, RIJKEBOER M, et al. Optical remote sensing of chlorophyll ain case2waters by use of an adaptive two-band algorithm with optimal error properties [J].Applied Optics,2001,40(21):3575-3585.
[69]GITELSON A A, SCHALLES J F, HLADIK C M. Remote chlorophyll-a retrieval in turbid,productive estuaries: Chesapeake Bay case study [J]. Remote Sensing of Environment,2007,109(4):464-472.
[70]D'SA E J, HU C, MULLER-KARGER F E, et al. Estimation of colored dissolved organicmatter and salinity fields in case2waters using SeaWiFS: Examples from Florida Bay andFlorida Shelf [J]. Proceedings of the Indian Academy of Sciences-Earth and Planetary Sciences,2002,111(3):197-207.
[71]HOOGENBOOM H J, DEKKER A G. The sensitivity of medium resolution imagingspectrometer (MERIS) for detecting chlorophyll and seston dry weight in coastal and inlandwaters; proceedings of the Igarss '98-1998International Geoscience and Remote SensingSymposium, Proceedings Vols1-5: Sensing and Managing the Environment, F,1998[C].
[72]HE M X, LIU Z S, DU K P, et al. Retrieval of chlorophyll from remote-sensing reflectancein the China seas [J]. Applied Optics,2000,39(15):2467-2474.
[73]MAGNUSON A, HARDING L W, MALLONEE M E, et al. Bio-optical model forChesapeake Bay and the Middle Atlantic Bight [J]. Estuarine Coastal and Shelf Science,2004,61(3):403-424.
[74]GORDON H R, BROWN O B, JACOBS M M. Computed relationships between theinherent and apparent optical properties of a flat homogeneous ocean [J]. Applied Optics,1975,14(2):417-427.
[75]LIU C C, MILLER R L. Spectrum matching method for estimating the chlorophyll-aconcentration, CDOM ratio, and backscatter fraction from remote sensing of ocean color [J].Canadian Journal of Remote Sensing,2008,34(4):343-355.
[76]ZHANG M, TANG J, DONG Q, et al. Retrieval of total suspended matter concentration inthe Yellow and East China Seas from MODIS imagery [J]. Remote Sensing of Environment,2010,114(2):392-403.
[77]KEINER L E, YAN X H. Aneural network model for estimating sea surface chlorophyll andsediments from thematic mapper imagery [J]. Remote Sensing of Environment,1998,66(2):153-165.
[78]TANAKA A, KISHINO M, OISHI T, et al. Application of neural network method to case IIwater; proceedings of the Remote Sensing of the Ocean and Sea Ice2000, F,2000[C].
[79]TANAKA A, KISHINO M, DOERFFER R, et al. Development of a neural networkalgorithm for retrieving concentrations of chlorophyll, suspended matter and yellow substancefrom radiance data of the ocean color and temperature scanner [J]. Journal of Oceanography,2004,60(3):519-530.
[80]ZHAN H, SHI P, CHEN C, et al. A Genetic Algorithm for Retrieval of Water Constituentsfrom Ocean Color Remote Sensed Data in Case2Waters [J]. Journal of Remote Sensing,2004,8(1):31-36.
[81]WOZNIAK S B, STRAMSKI D. Modeling the optical properties of mineral particlessuspended in seawater and their influence on ocean reflectance and chlorophyll estimation fromremote sensing algorithms [J]. Applied Optics,2004,43(17):3489-3503.
[82]YAN B, STAMNES K, TORATANI M, et al. Evaluation of a reflectance model used in theSeaWiFS ocean color algorithm: implications for chlorophyll concentration retrievals [J].Applied Optics,2002,41(30):6243-6259.
[83]DARECKI M, WEEKS A, SAGAN S, et al. Optical characteristics of two contrasting Case2waters and their influence on remote sensing algorithms [J]. Continental Shelf Research,2003,23(3-4):237-250.
[84]ANDRE J M, MORELA. ATMOSPHERIC CORRECTIONS AND INTERPRETATION OFMARINE RADIANCES IN CZCS IMAGERY, REVISITED [J]. Oceanologica Acta,1991,14(1):3-22.
[85]CHOMKO R M, GORDON H R, MARITORENAS, et al. Simultaneous retrieval of oceanicand atmospheric parameters for ocean color imagery by spectral optimization: a validation [J].Remote Sensing of Environment,2003,84(2):208-220.
[86]SCHROEDER T, SCHAALE M, FISCHER J. Retrieval of atmospheric and oceanicproperties from MERIS measurements: A new Case-2water processor for BEAM [J].International Journal of Remote Sensing,2007,28(24):5627-5632.
[87]ZHU J, CHEN J, MATSUSHITAB, et al. Atmospheric correction of ENVISAT/MERIS dataover case II waters: the use of black pixel assumption in oxygen and water vapour absorptionbands [J]. International Journal of Remote Sensing,2012,33(12):3713-3732.
[88]BERTHON J F, MORELA. VALIDATION OF ASPECTRALLIGHT-PHOTOSYNTHESISMODEL AND USE OF THE MODEL IN CONJUNCTION WITH REMOTELY SENSEDPIGMENT OBSERVATIONS [J]. Limnology and Oceanography,1992,37(4):781-796.
[89]杨煜,李云梅,王桥, et al.富营养化的太湖水体叶绿素a浓度模型反演[J].地球信息科学学报,2009,05):5597-5603.
[90]任春涛.基于遥感监测的湖泊富营养化状态的模糊模式识别研究[D];内蒙古农业大学,2007.
[91]潘德炉, J.F.G.GOWER,林寿仁.荧光法遥感海面叶绿素浓度的波段选择研究[J].海洋与湖沼,1989,06):564-570.
[92]潘德炉,林寿仁,高尔J F R, et al.利用荧光高度遥感海洋中叶绿素a的浓度[J].海洋学报(中文版),1989,11(06):780-787.
[93]潘德炉,王迪峰.我国海洋光学遥感应用科学研究的新进展[J].地球科学进展,2004,19(04):506-512.
[94]李铜基,陈清莲,朱建华.我国近岸II类海水光谱特性的测量与研究[J].海洋通报,2002,21(06):9-15.
[95]汪小勇,李铜基,唐军武, et al.二类水体表观光学特性的测量与分析——水面之上法方法研究[J].海洋技术,2004,23(02):1-6+23.
[96]唐军武,田国良.水色光谱分析与多成分反演算法[J].遥感学报,1997,1(04):252-256.
[97]刘堂友,匡定波,尹球.湖泊藻类叶绿素-a和悬浮物浓度的高光谱定量遥感模型研究[J].红外与毫米波学报,2004,23(01):11-15.
[98]黄家洁.基于MODIS数据的二类水体叶绿素反演算法理论及其实现[M].第十五届全国遥感技术学术交流会.中国贵阳.2005.
[99]万幼川,黄家洁,刘良明.基于MODIS数据的二类水体叶绿素反演算法理论与实现[J].武汉大学学报(信息科学版),2007,32(07):572-575.
[100]马超飞,蒋兴伟,唐军武, et al. HY-1CCD宽波段水色要素反演算法[J].海洋学报(中文版),2005,27(04):38-44.
[101]蒋刚,肖建,郑永康, et al.基于统计学习理论的海色遥感叶绿素浓度反演方法[J].中国海洋大学学报(自然科学版),2006,36(04):559-563.
[102]蒋刚,肖建,郑永康, et al.基于支持向量机的一类水域叶绿素a浓度反演研究[J].计算机应用,2005,25(10):2398-2400+2409.
[103]徐京萍,张柏,宋开山, et al.基于半分析模型的新庙泡叶绿素a浓度反演研究[J].红外与毫米波学报,2008,27(3):197-201.
[104]徐京萍,张柏,段洪涛, et al.新庙泡叶绿素a浓度高光谱定量模型研究[J].遥感技术与应用,2007,22(04):497-502.
[105]于堃,陆殿梅,熊黑钢.近7年来渤海海区冬季表层海水叶绿素浓度的遥感反演及其变化分析[J].遥感信息,2009,(06):55-62.
[106]曾炼.直接反演式水色遥感算法研究[D];华中科技大学,2009.
[107]刘斌.一类水域中从海面反射比反演叶绿素浓度垂直分布初探[D];中国海洋大学,2009.
[108]张彦喆,郑小慎,张波.渤海海域叶绿素浓度的遥感反演研究[J].天津科技大学学报,2010,25(01):51-53.
[109] NIELSEN E S. The Use of Radio-active Carbon (C14) for Measuring OrganicProduction in the Sea [J]. J Cons int Explor Mer,1952,18(2):117-140.
[110] RYTHER J H, YENTSCH C S. The estimation of phytoplankton production in theocean from chlorophyll and light data [J]. Limnology and Oceanography,1957,281-6.
[111] TALLING J. The phytoplankton population as a compound photosynthetic system [J].New phytologist,1957,56(2):133-149.
[112] CAD E G, HEGEMAN J. Primary production of phytoplankton in the Dutch WaddenSea [J]. Netherlands Journal of Sea Research,1974,8(2-3):240-259.
[113] BEHRENFELD M J, FALKOWSKI P G. A consumer's guide to phytoplankton primaryproductivity models [J]. Limnology and Oceanography,1997,42(7):1479-1491.
[114] SATHYENDRANATH S, PLATT T. Computation of aquatic primary production:extended formalism to include effect of angular and spectral distribution of light [J]. Limnologyand Oceanography,1989,34(01):188-198.
[115] SAKSHAUG E, ANDRESEN K, KIEFER D A. A steady state description of growthand light absorption in the marine planktonic diatom Skeletonema costatum [J]. Limnology andOceanography,1989,34(01):198-205.
[116] MOREL A. Available, usable, and stored radiant energy in relation to marinephotosynthesis [J]. Deep Sea Research,1978,25(8):673-688.
[117] FALKOWSKI P G, WOODHEAD A D. Primary productivity and biogeochemicalcycles in the sea [M]. Springer,1992.
[118] SMITH R C, BAKER K S. The bio-optical state of ocean waters and remote sensing [J].Limnology and Oceanography,1978,23(2):247-259.
[119] EPPLEY R, STEWART E, ABBOTT M R, et al. Estimating ocean primary productionfrom satellite chlorophyll. Introduction to regional differences and statistics for the SouthernCalifornia Bight [J]. Journal of Plankton Research,1985,7(1):57-70.
[120] FALKOWSKI P G. Light-shade adaptation and assimilation numbers [J]. Journal ofPlankton Research,1981,3(2):203-216.
[121] PLATT T, SATHYENDRANATH S. Estimators of primary production for interpretationof remotely sensed data on ocean color [J]. Journal of Geophysical Research: Oceans,1993,98(C8):14561-14576.
[122]唐世林,陈楚群,詹海刚.海洋初级生产力的遥感研究进展[J].台湾海峡,2006,25(4):591-598.
[123] SMITH R C, EPPLEY R W, BAKER K S. Correlation of primary production asmeasured aboard ship in Southern California Coastal waters and as estimated from satellitechlorophyll images [J]. Marine Biology.,1982,66(3):281-288.
[124]郝锵.中国近海叶绿素和初级生产力的时空分布特征和环境调控机制研究[D];中国海洋大学,2010.
[125] PLATT T. Primary production of the ocean water column as a function of surface lightintensity: algorithms for remote sensing [J]. Deep Sea Research,1986,33(2):149-163.
[126] MOREL A. Light and marine photosynthesis: a spectral model with geochemical andclimatological implications [J]. Progress in Oceanography,1991,26(3):263-306.
[127] SATHYENDRANATH S, LONGHURST A, CAVERHILL C M, et al. Regionally andseasonally differentiated primary production in the North Atlantic [J]. Deep Sea Research Part I:Oceanographic Research Papers,1995,42(10):1773-1802.
[128] LONGHURST A, SATHYENDRANATH S, PLATT T, et al. An estimate of globalprimary production in the ocean from satellite radiometer data [J]. Journal of Plankton Research,1995,17(6):1245-1271.
[129] ANTOINE D, ANDRT J M, MOREL A. Oceanic primary production2. Estimation atglobal scale from satellite (coastal zone color scanner) chlorophyll [J]. Global BiogeochemicalCycles,1996,10(1):57-69.
[130] BEHRENFELD M J, FALKOWSKI P G. Photosynthetic rates derived fromsatellite-based chlorophyll concentration [J]. Limnology and Oceanography,1997,42(1):1-20.
[131] WOZNIAK B, DERA J, FICEK D, et al. Modelling light and photosynthesis in themarine environment [J]. Oceanologia,2003,45(2):171-245.
[132] SMYTH T, TILSTONE G, GROOM S. Integration of radiative transfer into satellitemodels of ocean primary production [J]. Journal of Geophysical Research,2005,110(C10):C10014.
[133] BALCH W, EVANS R, BROWN J, et al. THE REMOTE-SENSING OF OCEANPRIMARY PRODUCTIVITY-USE OF A NEW DATA COMPILATION TO TESTSATELLITE ALGORITHMS [J]. Journal of Geophysical Research-Oceans,1992,97(C2):2279-2293.
[134] PLATT T, SATHYENDRANATH S. Oceanic primary production: estimation by remotesensing at local and regional scales [J]. Science,1988,241(4873):1613-1620.
[135] MUELLER J L, LANGE R E. Bio-optical provinces of the northeast Pacific Ocean: aprovisional analysis [J]. Limnology and Oceanography,1989,34(8):1572-1586.
[136]李国胜,邵宇宾.海洋初级生产力遥感与GIS评估模型研究[J].地理学报,1998,53(06):546-552
[137]官文江,何贤强,潘德炉, et al.渤、黄、东海海洋初级生产力的遥感估算[J].水产学报,2005,03):367-72.
[138]檀赛春,石广玉.中国近海初级生产力的遥感研究及其时空演化[J].地理学报,2006,61(11):1189-1199.
[139]曾呈奎,徐鸿儒,王春林.中国海洋志[M].郑州:大象出版社,2003.
[140]王昊寅.南黄海泥质区全新世底栖有孔虫及其环境意义[D];中国海洋大学,2012.
[141]国家海洋局908专项办公室.海洋生物生态调查技术规程[M].北京:海洋出版社,2006.
[142]樊辉.黄河口泥沙输移及三角洲的近期演变[D];中国科学院研究生院(海洋研究所),2005.
[143]樊辉,黄海军.南黄海辐射沙洲邻近海域表层悬浮颗粒物浓度遥感反演[J].地理科学,2011,31(02):159-165.
[144]唐军武,田国良,汪小勇, et al.水体光谱测量与分析Ⅰ:水面以上测量法[J].遥感学报,2004,8(01):37-44.
[145]王繁,周斌,徐建明, et al.杭州湾混浊水体表面光谱测量及光谱特征分析[J].光谱学与光谱分析,2009,29(3):730-734.
[146]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
[147]陈瑾,麻金继,王家成.海岸带混浊水域MODIS图像的大气校正[J].大气与环境光学学报,2007,2(4):306-311.
[148]陈军,孙记红,付军.基于分区暗像元和Spline插值方法估算太湖气溶胶光学厚度[J].遥感信息,2011,(03):33-37.
[149] PAN D, MAO Z. Atmospheric correction for China's coastal water colorremote sensing[J]. Acta Oceanologica Sinica,2001,20(03):343-354.
[150] GORDON H R, CASTA O D J. Aerosol analysis with the Coastal Zone Color Scanner:a simple method for including multiple scattering effects [J]. Applied Optics,1989,28(7):1320-1326.
[151] RUDOLF R. Afast atmospheric correction algorithm applied to Landsat TM images [J].International Journal of Remote Sensing1990,11(1):159-166.
[152]焦红波,查勇,李云梅, et al.基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型[J].遥感学报,2006,10(002):242-248.
[153]荀尚培,翟武全,范伟. MODIS巢湖水体叶绿素a浓度反演模型[J].应用气象学报,2009,20(1):95-101.
[154] JOINT I, GROOM S B. Estimation of phytoplankton production from space: currentstatus and future potential of satellite remote sensing [J]. Journal of Experimental MarineBiology and Ecology,2000,250(1):233-255.
[155]段洪涛,张柏,宋开山, et al.查干湖叶绿素a浓度高光谱定量模型研究[J].环境科学,2006,27(3):503-507.
[156] SATHYENDRANATH S, PRIEUR L, MOREL A. A three-component model of oceancolour and its application to remote sensing of phytoplankton pigments in coastal waters [J].International Journal of Remote Sensing,1989,10(8):1373-1394.
[157] GORDON H R. DEPENDENCE OF THE DIFFUSE REFLECTANCE OFNATURAL-WATERS ON THE SUN ANGLE [J]. Limnology and Oceanography,1989,34(8):1484-1489.
[158] MOREL A, GENTILI B. DIFFUSE-REFLECTANCE OF OCEANIC WATERS.2.BIDIRECTIONALASPECTS [J].Applied Optics,1993,32(33):6864-7689.
[159] MOREL A, VOSS K J, GENTILI B. BIDIRECTIONAL REFLECTANCE OFOCEANIC WATERS-A COMPARISON OF MODELED AND MEASURED UPWARDRADIANCE FIELDS [J]. Journal of Geophysical Research-Oceans,1995,100(C7):13143-13150.
[160] MOREL A, GENTILI B. DIFFUSE REFLECTANCE OF OCEANIC WATERS-ITSDEPENDENCE ON SUN ANGLE AS INFLUENCED BY THE MOLECULAR-SCATTERINGCONTRIBUTION [J].Applied Optics,1991,30(30):4427-4438.
[161] MARITORENA S, MOREL A, GENTILI B. DIFFUSE-REFLECTANCE OFOCEANIC SHALLOW WATERS-INFLUENCE OF WATER DEPTH AND BOTTOMALBEDO [J]. Limnology and Oceanography,1994,39(7):1689-1703.
[162] JIN Z H, CHARLOCK T P, RUTLEDGE K, et al. Analytical solution of radiativetransfer in the coupled atmosphere-ocean system with a rough surface [J]. Applied Optics,2006,45(28):7443-7455.
[163]唐军武,田国良,陈清莲.离水辐射非朗伯特性的Monte Carlo模拟及分析[J].海洋学报(中文版),2000,22(02):48-57.
[164]唐军武.海洋光学特性模拟与遥感模型[D].北京;中国科学院遥感应用研究所,1999.
[165] LING Z, ZHOU B, JIANG J, et al. Modeling the Bidirectional Reflectance DistributionFunction (BRDF) of water based on Monte Carlo simulation [M]//TONG Q G X Z B. RemoteSensing of the Environment: The17th China Conference on Remote Sensing.2011.
[166]徐希孺.遥感物理[M].北京大学出版社,2005.
[167]刘玉光.卫星海洋学[M].北京:高等教育出版社,2009.
[168] COX C, MUNK W. MEASUREMENT OF THE ROUGHNESS OF THE SEASURFACE FROM PHOTOGRAPHS OF THE SUNS GLITTER [J]. Journal of the OpticalSociety ofAmerica,1954,44(11):838-850.
[169]何晓群.多元统计分析[M].北京:中国人民大学出版社,2004.
[170]徐文科,仲莉娜.概率论与数理统计[M].哈尔滨:东北林业大学出版社,2003.
[171]屈鸿.回复式神经网络及其在组合优化问题中的应用[D];电子科技大学,2006.
[172]刘旭升,李锋,昝国胜, et al.基于GIS和神经网络的森林植被分类(英文)[J].遥感学报,2007,11(05):710-717.
[173]李永亮,林辉,孙华, et al.基于BP神经网络的森林树种分类研究[J].中南林业科技大学学报,2010,30(11):43-46.
[174]李健,麻金继,吉玮.基于广义回归神经网络反演悬浮泥沙含量的定量遥感方法[J].大气与环境光学学报,2010,5(04):305-310.
[175]詹海刚,平施,陈楚群.利用神经网络反演海水叶绿素浓度[J].科学通报,2000,45(17):1879-1884.
[176]李伟.人工神经网络方法反演东中国海悬移质浓度[J].海洋湖沼通报,2007,(04):55-58.
[177]仲京臣,汪小勇,陈清莲.基于神经网络的黄东海春季二类水体三要素浓度反演方法[J].海洋技术,2005,24(04):118-122.
[178]丛丕福,牛铮,曲丽梅, et al.基于神经网络和TM图像的大连湾海域悬浮物质量浓度的反演[J].海洋科学,2005,29(04):31-35.
[179]张亭禄,李肖霞.基于人工神经网络的海水漫射衰减系数的遥感反演方法[J].中国海洋大学学报(自然科学版),2007,37(04):676-680.
[180]曲元.海洋污染水体的卫星遥感监测方法研究[D];大连海事大学,2000.
[181]卢晓亭,孙勇,笪良龙, et al.基于EMD的BP神经网络海水温度时间序列预测研究[J].海洋技术,2009,28(03):79-82.
[182]韩震,赵宁.基于LM-BP神经网络的Argo数据西北太平洋海水温度模型[J].海洋环境科学,2012,31(04):555-560.
[183]孙凌.针对HY-1ACCD的大气修正与水体组分反演[D];中国科学院研究生院（海洋研究所）,2005.
[184]王丽静. AMSR-E卫星海面气象参数遥感方法研究[D];中国科学院研究生院（海洋研究所）,2008.
[185]叶世伟,史忠植.神经网络原理[M].北京:机械工程出版社,2006.
[186]沈花玉,王兆霞,高成耀, et al. BP神经网络隐含层单元数的确定[J].天津理工大学学报,2008,24(05):13-15.
[187]杨曦光.高光谱数据提取森林冠层叶绿素及氮含量的研究[D];东北林业大学,2010.
[188]吴昌友.神经网络的研究及应用[D];东北农业大学,2007.
[189]郭海涛,张殿伦,马国芳, et al.使用BP算法时应考虑的若干问题[J].佳木斯大学学报(自然科学版),2000,18(04):363-365.
[190]夏克文,李昌彪,沈钧毅.前向神经网络隐含层节点数的一种优化算法[J].计算机科学,2005,32(10):143-145.
[191]徐小军.基于LANDSAT TM影像毛竹林地上部分碳储量估算研究[D];浙江林学院,2009.
[192]杨博,王亚东,苏小红.一种基于误差放大的快速BP学习算法[J].计算机研究与发展,2004,41(05):774-779.
[193]夏爱国. BP改进算法研究及一种系统控制训练算法[J].系统仿真学报,1999,10(02):61-4+74.
[194]金丕彦,芮勇. BP算法各种改进算法的研究及应用[J].南京航空航天大学学报,1994,26(S1):201-205.
[195]徐小军,杜华强,周国模, et al. Erf-BP混合像元分解及在森林遥感信息提取中应用[J].林业科学,2011,47(02):30-38.
[196]范文义,张海玉,于颖, et al.三种森林生物量估测模型的比较分析[J].植物生态学报,2011,35(04):402-410.
[197]杨曦光,于颖,黄海军, et al.森林冠层氮含量遥感估算[J].红外与毫米波学报,2012,31(06):536-543.
[198]张江涛,殷克东.黄海春季表层叶绿素和初级生产力及其粒径结构研究[J].生态环境学报,2010,19(09):2107-2111.
[199]郑国侠,宋金明,戴纪翠, et al.南黄海秋季叶绿素a的分布特征与浮游植物的固碳强度[J].海洋学报(中文版),2006,28(03):109-118.
[200]朱明远,毛兴华,吕瑞华, et al.黄海海区的叶绿素a和初级生产力[J].黄渤海海洋,1993,11(03):38-51.
[201]李泽军.从水体叶绿素含量评价于桥水库富营养化程度[J].河北水利水电技术,2001,(06):44-45.
[202]张穗,何报寅.河口Ⅱ类水体富营养化的遥感定量方法研究[J].长江科学院院报,2004,21(03):29-31.
[203]吴传庆,盖嘉翔,王桥, et al.湖泊富营养化遥感评价模型的建立方法[J].中国环境监测,2011,27(05):77-82.
[204]王志辉.基于MODIS的淡水湖泊富营养化状况遥感监测[D];华中科技大学,2007.
[205]苗群.南四湖水环境质量评价研究[D];青岛大学,2008.
[206]牟秀菊,黄连光,于洋, et al.日照市近岸海域水质变化趋势调查及评价[J].河北渔业,2010,(11):35-38.
[207]牛显春,周建敏,林哲晖.茂名近岸海域富营养化检测与评价[J].茂名学院学报,2006,16(06):10-13.
[208]杨建设,张家寿,杨孝军, et al.茂名市近海养殖区水环境质量的检测[J].茂名学院学报,2005,15(01):1-4+29.
[209]王保栋,孙霞,韦钦胜, et al.我国近岸海域富营养化评价新方法及应用[J].海洋学报(中文版),2012,34(04):61-66.
[210]王保栋,战闰,藏家业.黄海、东海浮游植物生长的营养盐限制性因素初探[J].海洋学报(中文版),2003,25(S2):190-195.
[211]高爽,李正炎.北黄海夏、冬季叶绿素和初级生产力的空间分布和季节变化特征[J].中国海洋大学学报(自然科学版),2009,39(04):604-610.
[212] CONG P, QU L, HAN G, et al. Remotely sensed detection and application analysis ofocean particulate organic carbon [J]. Marine Environmental Science,2012,31(2):300-304.
[213] SHANG S, LEE Z, WEI G. Characterization of MODIS-derived euphotic zone depth:Results for the China Sea [J]. Remote Sensing of Environment,2011,115(1):180-186.
[214] STRAMSKA M. Particulate organic carbon in the global ocean derived from SeaWiFSocean color [J]. Deep-Sea Research Part I-Oceanographic Research Papers,2009,56(9):1459-1470.
[215]唐世林,陈楚群,詹海刚.海洋初级生产力的遥感研究进展[J].台湾海峡,2006,25(04):591-598.
[216]邹亚荣,马超飞,邵岩.遥感海洋初级生产力的研究进展[J].遥感信息,2005,(02):58-61.
[217]费尊乐,李宝华.叶绿素a与初级生产力之间的相关关系[J].青岛海洋大学学报,1990,20(01):73-80.
[218]费尊乐, C．C．TREES,李宝华.利用叶绿素资料计算初级生产力[J].黄渤海海洋,1997,15(01):35-47.
[219] PARSONS T R, TAKAHASHI M, HARGRAVE B. Biological oceanographic processes[M]. OXFORD(UK: PERGAMON PRESS,1983.
[220]李国胜,王芳,梁强, et al.东海初级生产力遥感反演及其时空演化机制[J].地理学报,2003,58(04):483-493.
[221]李小斌,陈楚群,施平, et al.南海1998——2002年初级生产力的遥感估算及其时空演化机制[J].热带海洋学报,2006,25(03):57-62.
[222]邵宇宾,李国胜.遥感与地理信息系统在海洋初级生产力研究中的应用[J].地理学报,1995,50(S1):119-125.
[223]殷燕,张运林,时志强, et al.基于VGPM模型和MODIS数据估算梅梁湾浮游植物初级生产力[J].生态学报,2012,32(11):3528-3537.
[224] SISWANTO E, ISHIZAKA J, YOKOUCHI K. Optimal primary production model andparameterization in the eastern East China Sea [J]. Journal of Oceanography,2006,62(03):361-372
[225] LI G, GAO P, WANG F, et al. Estimation of ocean primary productivity and itsspatio-temporal variation mechanism for East China Sea based on VGPM model [J]. Journal ofGeographical Sciences,2004,14(S1):34-42.
[226] ISHIZAKA J, SISWANTO E, ITOH T, et al. Verification of vertically generalizedproduction model and estimation of primary production in Sagami Bay, Japan [J]. Journal ofOceanography,2007,63(03):517-524.
[227] KAMEDA T, ISHIZAKA J. Size-Fractionated Primary Production Estimated by aTwo-Phytoplankton Community Model Applicable to Ocean Color Remote Sensing [J]. Journalof Oceanography,2005,61(04):663-672.
[228] FROUIN R, FRANZ B, WANG M. Algorithm to estimate PAR from SeaWiFS dataVersion1.2-Documentation [J]. NASA GSFC, available at http://oceancolor gsfc nasagov/DOCS/seawifs_par_wfigs pdf,2001,
[229]贾艳红,孙莹,牛博颖.基于RS和GIS的黄海海区海洋初级生产力估算[J].淮海工学院学报(自然科学版),2010,19(04):87-91.
[230] GARCIA-SOTO C, PINGREE R D. Spring and summer blooms of phytoplankton(SeaWiFS/MODIS) along a ferry line in the Bay of Biscay and western English Channel [J].Continental Shelf Research,2009,29(8):1111-1122.
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