结合光吸收的近海水体硅藻浓度反演模型构建
Inversion model of diatom concentrations in coastal water based on absorption coefficients
- 2023年27卷第2期 页码:363-375
纸质出版日期: 2023-02-07
DOI: 10.11834/jrs.20210475
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2023-02-07 ,
扫 描 看 全 文
樊杰,孙德勇,王胜强,张海龙,环宇,何宜军.2023.结合光吸收的近海水体硅藻浓度反演模型构建.遥感学报,27(2): 363-375
Fan J,Sun D Y,Wang S Q,Zhang H L,Huan Y and He Y J. 2023. Inversion model of diatom concentrations in coastal water based on absorption coefficients. National Remote Sensing Bulletin, 27(2):363-375
浮游植物种群结构对了解海洋碳循环,生物多样性等研究具有重要意义。针对某种浮游植物种群生物量的研究,可以掌握浮游植物种群结构变化特征。本研究基于航次实测色素数据,利用CHEMTAX方法获取近海水体硅藻浓度信息,分析实测硅藻浓度数据的空间分布状态。对高斯模型中心波长和半波宽参数进行优化,结合高斯模型分解获取的高斯峰值,分析其与硅藻浓度之间的相关关系,并遴选相关系数较高的高斯峰值。利用不同数学函数,开展高斯峰值与硅藻浓度之间的关系模型构建研究,并结合精度指标(决定系数
R
2
,中值误差ME)判定各数学函数模型的硅藻浓度反演精度,以提出最优的反演模型。研究结果表明:(1)实测硅藻浓度整体上呈现出近岸高,离岸低的空间分布特征,高值区域主要分布在渤海湾及长江口附近;(2)硅藻浓度反演模型精度较高,通过留一交叉验证法对模型进行验证,决定系数为0.79,中值误差为49%,
p
值小于0.001,该模型为后续开展卫星遥感反演硅藻浓度的工作奠定了基础。
Phytoplankton are a vital component of marine organisms and play a crucial role in material circulation
energy flow and information transmission. The study of phytoplankton communities is important to understand carbon cycling and biodiversity
and the study of the biomass for a certain phytoplankton group can help understand the structural changes of phytoplankton communities. Diatoms are the dominant taxonomic group with the most abundant concentration in coastal water
and diatoms provide solid photosynthetic capacity (approximately 40% of marine primary productivity). Meanwhile
diatom concentrations will indirectly affect the breeding progress of holoplankton. In this study
observation samples (
n
= 252) were collected in the marginal seas of the northwest Pacific Ocean
covering the Bohai Sea (BS)
Yellow Sea (YS)
and East China Sea (ECS)
from six cruise surveys that covered different seasons. The concentration of diatoms was calculated by phytoplankton pigment concentrations collected with the High-Performance Liquid Chromatography (HPLC) method with the CHEMTAX method
and the spatial distribution of measured diatom concentrations was analyzed. Based on
in situ
data parameters collected by multiple cruises
including phytoplankton absorption coefficients and diatom concentrations
we optimized Gaussian central bands and half-wave widths with derivative spectrum analysis and the peaks of the specific absorption spectrum
which were mentioned in existing research. Then
the correlation coefficients between diatom concentrations and different Gaussian peaks
which were decomposed by Gaussian function
were analyzed. We established Gaussian peaks
which had higher correlation coefficients with diatom concentrations
to establish an inversion model of diatom concentrations. The inversion models between Gaussian peaks and diatom concentrations were studied with different mathematical functions (linear function
quadratic function
logarithmic function
exponential function and power function)
and the accuracy for each model was evaluated by two precision indicators (determination coefficients
R
2
and median percent error
ME) to propose the optimal inversion model form. The parameters (
A
B
) were confirmed by leave-one-out methods later. The results of the research show the following: (1) The spatial distribution of measured diatom concentrations presented characteristics that were higher nearshore and lower offshore. The higher areas for BS appeared in the neighborhood
and the trend to the central area was decreasing progressively. Similar to BS
the spatial distribution of measured diatom concentrations was higher nearshore (Yangtze Estuary) and lower offshore (central area) in both the YS and ECS. (2) The evaluation indicators for our diatom concentration inversion model were relatively high. Comparing the diatom concentrations modeled in this study and calculated by the CHEMTAX method and using leave-one-out methods to verify the accuracy between them
the determination coefficient (
R
2
) was 0.79
the Median Error (ME) was 49%
and the
p
value was under 0.001. It should be noted that some deviation appears in the lower concentration (
<
0.02 mg m
-3
); that is why the range of application should be concluded in later applications. In summary
the relatively high accuracy shows the applicability of the inversion model of diatom concentrations in this study
and the model lays the foundation for conducting research on satellite remote sensing inversion of diatom concentrations.
遥感硅藻浓度高斯模型CHEMTAX中国近海
remote sensingdiatoms concentrationGaussian modelCHEMTAXChinese marginal seas
Aiken J, Pradhan Y, Barlow R, Lavender S, Poulton A, Holligan P and Hardman-Mountford N. 2009. Phytoplankton pigments and functional types in the Atlantic Ocean: a decadal assessment, 1995-2005. Deep Sea Research Part II: Topical Studies in Oceanography, 56(15): 899-917 [DOI: 10.1016/j.dsr2.2008.09.017http://dx.doi.org/10.1016/j.dsr2.2008.09.017]
Bouman H, Platt T, Sathyendranath S and Stuart V. 2005. Dependence of light-saturated photosynthesis on temperature and community structure. Deep Sea Research Part I: Oceanographic Research Papers, 52(7): 1284-1299 [DOI: 10.1016/j.dsr.2005.01.008http://dx.doi.org/10.1016/j.dsr.2005.01.008]
Bracher A, Taylor M H, Taylor B, Dinter T, Röttgers R and Steinmetz F. 2015. Using empirical orthogonal functions derived from remote-sensing reflectance for the prediction of phytoplankton pigment concentrations. Ocean Science, 11(1): 139-158 [DOI: 10.5194/os-11-139-2015http://dx.doi.org/10.5194/os-11-139-2015]
Brewin R J W, Hardman-Mountford N J, Lavender S J, Raitsos D E, Hirata T, Uitz J, Devred E, Bricaud A, Ciotti A and Gentili B. 2011. An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing. Remote Sensing of Environment, 115(2): 325-339 [DOI: 10.1016/j.rse.2010.09.004http://dx.doi.org/10.1016/j.rse.2010.09.004]
Brotas V, Brewin R J W, Sá C, Brito A C, Silva A, Mendes C R, Diniz T, Kaufmann M, Tarran G, Groom S B, Platt T and Sathyendranath S. 2013. Deriving phytoplankton size classes from satellite data: validation along a trophic gradient in the eastern Atlantic Ocean. Remote Sensing of Environment, 134: 66-77 [DOI: 10.1016/j.rse.2013.02.013http://dx.doi.org/10.1016/j.rse.2013.02.013]
Bruckman L S, Richardson T L, Swanstrom J A, Donaldson K A, Allora Jr M, Shaw T J and Myrick M L. 2012. Linear discriminant analysis of single-cell fluorescence excitation spectra of five phytoplankton species. Applied Spectroscopy, 66(1): 60-65 [DOI: 10.1366/11-06294http://dx.doi.org/10.1366/11-06294]
Chase A, Boss E, Zaneveld R, Bricaud A, Claustre H, Ras J, Dall’Olmo G and Westberry T K. 2013. Decomposition of in situ particulate absorption spectra. Methods in Oceanography, 7: 110-124 [DOI: 10.1016/j.mio.2014.02.002http://dx.doi.org/10.1016/j.mio.2014.02.002]
Chase A P, Boss E, Cetinić I and Slade W. 2017. Estimation of phytoplankton accessory pigments from hyperspectral reflectance spectra: toward a global algorithm. Journal of Geophysical Research, 122(12): 9725-9743 [DOI: 10.1002/2017JC012859http://dx.doi.org/10.1002/2017JC012859]
Chen C T A. 2009. Chemical and physical fronts in the Bohai, Yellow and East China seas. Journal of Marine Systems, 78(3): 394-410 [DOI: 10.1016/j.jmarsys.2008.11.016http://dx.doi.org/10.1016/j.jmarsys.2008.11.016]
Chen Z Y, Li J F, Shen H T and Wang Z H. 2001. Yangtze River of China: historical analysis of discharge variability and sediment flux. Geomorphology, 41(2/3): 77-91 [DOI: 10.1016/S0169-555X(01)00106-4http://dx.doi.org/10.1016/S0169-555X(01)00106-4]
Devred E, Sathyendranath S, Stuart V and Platt T. 2011. A three component classification of phytoplankton absorption spectra: Application to ocean-color data. Remote Sensing of Environment, 115(9): 2255-2266 [DOI: 10.1016/j.rse.2011.04.025http://dx.doi.org/10.1016/j.rse.2011.04.025]
Doxaran D, Froidefond J M, Lavender S and Castaing P. 2002. Spectral signature of highly turbid waters: application with SPOT data to quantify suspended particulate matter concentrations. Remote Sensing of Environment, 81(1): 149-161 [DOI: 10.1016/S0034-4257(01)00341-8http://dx.doi.org/10.1016/S0034-4257(01)00341-8]
Falkowski P G, Barber R T and Smetacek V. 1998. Biogeochemical controls and feedbacks on ocean primary production. Science, 281(5374): 200-206
Ficek D, Kaczmarek S, Stoń-Egiert J, Woźniak B, Majchrowski R and Dera J. 2004. Spectra of light absorption by phytoplankton pigments in the Baltic; conclusions to be drawn from a Gaussian analysis of empirical data. Oceanologia, 46(4): 533-555
Furuya K, Hayashi M, Yabushita Y and Ishikawa A. 2003. Phytoplankton dynamics in the East China Sea in spring and summer as revealed by HPLC-derived pigment signatures. Deep Sea Research Part II: Topical Studies in Oceanography, 50(2): 367-387 [DOI: 10.1016/S0967-0645(02)00460-5http://dx.doi.org/10.1016/S0967-0645(02)00460-5]
Gao Y H, Yu Q B, Qi Y Z, Zou J Z, Lu D D, Li Y and Chen C P. 2003. Species composition and ecological distribution of planktonic diatoms in the Changjiang River estuary during Spring. Chinese Journal of Applied Ecology, 14(7): 1044-1048
高亚辉, 虞秋波, 齐雨藻, 邹景忠, 陆斗定, 李扬, 陈长平. 2003. 长江口附近海域春季浮游硅藻的种类组成和生态分布. 应用生态学报, 14(7): 1044-1048
Graff J R, Westberry T K, Milligan A J, Brown M B, Dall'Olmo G, van Dongen-Vogels V, Reifel K M and Behrenfeld M J. 2015. Analytical phytoplankton carbon measurements spanning diverse ecosystems. Deep Sea Research Part I: Oceanographic Research Papers, 102: 16-25 [DOI: 10.1016/j.dsr.2015.04.006http://dx.doi.org/10.1016/j.dsr.2015.04.006]
Gurney K R, Law R M, Denning A S, Rayner P J, Baker D, Bousquet P, Bruhwiler L, Chen Y H, Ciais P, Fan S M, Fung I Y, Gloor M, Heimann M, Higuchi K, John J, Maki T, Maksyutov S, Masarie K, Peylin P, Prather M, Pak B C, Randerson J, Sarmiento J, Taguchi S, Takahashi T and Yuen C W. 2002. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature, 415(6872): 626-630 [DOI: 10.1038/415626ahttp://dx.doi.org/10.1038/415626a]
Hirata T, Aiken J, Hardman-Mountford N, Smyth T J and Barlow R G. 2008. An absorption model to determine phytoplankton size classes from satellite ocean colour. Remote Sensing of Environment, 112(6): 3153-3159 [DOI: 10.1016/j.rse.2008.03.011http://dx.doi.org/10.1016/j.rse.2008.03.011]
Hirawake T, Takao S, Horimoto N, Ishimaru T, Yamaguchi Y and Fukuchi M. 2011. A phytoplankton absorption-based primary productivity model for remote sensing in the Southern Ocean. Polar Biology, 34(2): 291-302 [DOI: 10.1007/s00300-010-0949-yhttp://dx.doi.org/10.1007/s00300-010-0949-y]
Hoepffner N and Sathyendranath S. 1991. Effect of pigment composition on absorption properties of phytoplankton. Marine Ecology Progress Series, 73: 11-23 [DOI: 10.3354/meps073011http://dx.doi.org/10.3354/meps073011]
House J I, Prentice I C and Quéré C L. 2002. Maximum impacts of future reforestation or deforestation on atmospheric CO2. Global Change Biology, 8(11): 1047-1052 [DOI: 10.1046/j.1365-2486.2002.00536.xhttp://dx.doi.org/10.1046/j.1365-2486.2002.00536.x]
Ichikawa H and Chaen M. 2000. Seasonal variation of heat and freshwater transports by the Kuroshio in the East China Sea. Journal of Marine Systems, 24(1/2): 119-129 [DOI: 10.1016/S0924-7963(99)00082-2http://dx.doi.org/10.1016/S0924-7963(99)00082-2]
Irwin A J, Finkel Z V, Müller-Karger F E and Troccoli Ghinaglia L. 2015. Phytoplankton adapt to changing ocean environments. Proceedings of the National Academy of Sciences of the United States of America, 112(18): 5762-5766 [DOI: 10.1073/pnas.1414752112http://dx.doi.org/10.1073/pnas.1414752112]
Jónasdóttir S H. 1994. Effects of food quality on the reproductive success of Acartia tonsa and Acartia hudsonica: laboratory observations. Marine Biology, 121(1): 67-81 [DOI: 10.1007/BF00349475http://dx.doi.org/10.1007/BF00349475]
Lee Z P, Carder K L and Arnone R A. 2002. Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Appl Opt, 41 (27): 5755-5772 [DOI: 10.1364/AO.41.005755http://dx.doi.org/10.1364/AO.41.005755]
Lee Z P. 2006. Remote Sensing of Inherent Optical Properties: Fundamentals, Tests of Algorithms, and Applications. Dartmouth: International Ocean Colour Coordinating Group (IOCCG) [DOI: 10.25607/OBP-96http://dx.doi.org/10.25607/OBP-96]
Li J and Li C L. 2004. Deleterious effects of diatom in high concentration on copepod reproduction. Acta Ecologica Sinica, 24(11): 2664-2670
李捷, 李超伦. 2004. 高浓度硅藻对桡足类繁殖的抑制作用. 生态学报, 24(11): 2664-2670 [DOI: 10.3321/j.issn:1000-0933.2004.11.046http://dx.doi.org/10.3321/j.issn:1000-0933.2004.11.046]
Li N, Sun D Y, Huan Y, Wang S Q, Zhang H L, Qiu Z F and He Y J. 2020. Determination and application of specific absorption spectra of phytoplankton species in Yellow Sea and Bohai Sea. Acta Optica Sinica, 40(6): 0601004
李楠, 孙德勇, 环宇, 王胜强, 张海龙, 丘仲锋, 何宜军. 2020. 黄渤海浮游植物种群比吸收光谱的确定及其应用. 光学学报, 40(6): 0601004 [DOI: 10.3788/AOS202040.0601004http://dx.doi.org/10.3788/AOS202040.0601004]
Li W K W and Dickie P M. 2001. Monitoring phytoplankton, bacterioplankton, and virioplankton in a coastal inlet (Bedford Basin) by flow cytometry. Cytometry, 44(3): 236-246 [DOI: 10.1002/1097-0320(20010701)44:3<236::AID-CYTO1116>3.0.CO;2-5http://dx.doi.org/10.1002/1097-0320(20010701)44:3<236::AID-CYTO1116>3.0.CO;2-5]
Liang Y J, Li J, Zhang L and Li L N. 2009. The effects of diatom in high concentration on copepod reproduction. Transactions of Oceanology and Limnology, (4): 83-92
梁彦娟, 李捷, 张乐, 李洛娜. 2009. 高浓度硅藻对桡足类繁殖的影响. 海洋湖沼通报, (4): 83-92 [DOI: 10.3969/j.issn.1003-6482.2009.04.010http://dx.doi.org/10.3969/j.issn.1003-6482.2009.04.010]
Lin P M. 2008. Study on Distribution and Speciation of Silica in Tonkin Gulf. Xiamen: Xiamen University
林培梅. 2008. 北部湾海域硅的分布与赋存形态研究. 厦门: 厦门大学
Ling Z B, Sun D Y, Wang S Q, Qiu Z F, Huan Y, Mao Z H and He Y J. 2018. Retrievals of phytoplankton community structures from in situ fluorescence measurements by HS-6P. Optics Express, 26(23): 30556-30575 [DOI: 10.1364/OE.26.030556http://dx.doi.org/10.1364/OE.26.030556]
Litchman E. 2007. Resource competition and the ecological success of phytoplankton//Falkowski P G and Knoll A H, eds. Evolution of Primary Producers in the Sea. Amsterdam: Academic Press: 351-375 [DOI: 10.1016/B978-012370518-1/50017-5http://dx.doi.org/10.1016/B978-012370518-1/50017-5]
Liu L H. 2007. The Community Structure and Diversity Analysis of Phytoplankton in the Yellow Sea and the Chang Jiang Estuary Waters. Qingdao: Ocean University of China
柳丽华. 2007. 黄海及长江口毗邻海域浮游植物群落结构和多样性分析. 青岛: 中国海洋大学
Liu S X, Sui W N, Sun S Y, Lin Y and Li D M. 2015. The community structure and seasonal distribution of phytoplankton in the coast of North Yellow Sea. Transactions of Oceanology and Limnology, (2): 128-138
刘述锡, 隋伟娜, 孙淑艳, 林勇, 李冬梅. 2015. 北黄海北部近岸海域网采浮游植物群落结构. 海洋湖沼通报, (2): 128-138 [DOI: 10.13984/j.cnki.cn37-1141.2015.02.018http://dx.doi.org/10.13984/j.cnki.cn37-1141.2015.02.018]
Mackey M D, Mackey D J, Higgins H W and Wright S W. 1996. CHEMTAX - A program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Marine Ecology Progress Series, 144: 265-283 [DOI: 10.3354/meps144265http://dx.doi.org/10.3354/meps144265]
Moisan J R, Moisan T A H and Linkswiler M A. 2011. An inverse modeling approach to estimating phytoplankton pigment concentrations from phytoplankton absorption spectra. Journal of Geophysical Research, 116(C9): C09018 [DOI: 10.1029/2010JC006786http://dx.doi.org/10.1029/2010JC006786]
Morel A and Bricaud A. 1981. Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton. Deep Sea Research Part A. Oceanographic Research Papers, 28(11): 1375-1393 [DOI: 10.1016/0198-0149(81)90039-Xhttp://dx.doi.org/10.1016/0198-0149(81)90039-X]
Sathyendranath S, Watts L, Devred E, Platt T, Caverhill C and Maass H. 2004. Discrimination of diatoms from other phytoplankton using ocean-colour data. Marine Ecology Progress Series, 272: 59-68 [DOI: 10.3354/meps272059http://dx.doi.org/10.3354/meps272059]
Scala S and Bowler C. 2001. Molecular insights into the novel aspects of diatom biology. Cellular and Molecular Life Sciences CMLS, 58(11): 1666-1673 [DOI: 10.1007/PL00000804http://dx.doi.org/10.1007/PL00000804]
Shen F, Salama S, Zhou Y X, Li J F, Su Z B and Kuang D B. 2010. Remote-sensing reflectance characteristics of highly turbid estuarine waters – a comparative experiment of the Yangtze River and the Yellow River. International Journal of Remote Sensing, 31(10): 2639-2654 [DOI: 10.1080/01431160903085610http://dx.doi.org/10.1080/01431160903085610]
Shen F, Zhou Y X, Li J F and Liu X L. 2009. Theoretical analysis and experimental observation for the effect of suspended sediment particle size on remote-sensing reflectance. Journal of Infrared and Millimeter Waves, 28(3): 168-172
沈芳, 周云轩, 李九发, 刘小丽. 2009. 河口悬沙粒径对遥感反射率影响的理论分析与实验观测. 红外与毫米波学报, 28(3): 168-172 [DOI: 10.3321/j.issn:1001-9014.2009.03.003http://dx.doi.org/10.3321/j.issn:1001-9014.2009.03.003]
Starr M, Runge J A and Therriault J C. 1999. Effects of diatom diets on the reproduction of the planktonic copepod Calanus finmarchicus. Sarsia, 84(5/6): 379-389 [DOI: 10.1080/00364827.1999.10807345http://dx.doi.org/10.1080/00364827.1999.10807345]
Stevenson R J, Peterson C G, Kirschtel D B, King C C and Tuchman N C. 1991. Density-dependent growth, ecological strategies, and effects of nutrients and shading on benthic diatom succession in streams. Journal of Phycology, 27(1): 59-69 [DOI: 10.1111/j.0022-3646.1991.00059.xhttp://dx.doi.org/10.1111/j.0022-3646.1991.00059.x]
Su J L. 2001. A review of circulation dynamics of the coastal oceans near China. Acta Oceanologica Sinica, 23(4): 1-16
苏纪兰. 2001. 中国近海的环流动力机制研究. 海洋学报, 23(4): 1-16 [DOI: 10.3321/j.issn:0253-4193.2001.04.001http://dx.doi.org/10.3321/j.issn:0253-4193.2001.04.001]
Sun D Y, Huan Y, Qiu Z F, Hu C M, Wang S Q and He Y J. 2017. Remote-sensing estimation of phytoplankton size classes from GOCI satellite measurements in Bohai Sea and Yellow Sea. Journal of Geophysical Research, 122(10): 8309-8325 [DOI: 10.1002/2017JC013099http://dx.doi.org/10.1002/2017JC013099]
Sun J, Liu D Y, Yang S M, Guo J and Qian S B. 2002. The preliminary study on phytoplankton community structure in the central Bohai sea and the Bohai strait and its adjacent area. Oceanologia et Limnologia Sinica, 33(5): 461-471
孙军, 刘东艳, 杨世民, 郭健, 钱树本. 2002. 渤海中部和渤海海峡及邻近海域浮游植物群落结构的初步研究. 海洋与湖沼, 33(5): 461-471 [DOI: 10.3321/j.issn:0029-814X.2002.05.002http://dx.doi.org/10.3321/j.issn:0029-814X.2002.05.002]
Tang Y X, Zhou E M, Li X Z and Li Z X. 2000. Some features of circulation in the southern Huanghai Sea. Acta Oceanologica Sinica, 22(1): 1-16
汤毓祥, 邹娥梅, 李兴宰, 李载学. 2000. 南黄海环流的若干特征. 海洋学报, 22(1): 1-16 [DOI: 10.3321/j.issn:0253-4193.2000.01.001http://dx.doi.org/10.3321/j.issn:0253-4193.2000.01.001]
Uitz J, Claustre H, Morel A and Hooker S B. 2006. Vertical distribution of phytoplankton communities in open ocean: an assessment based on surface chlorophyll. Journal of Geophysical Research, 111(C8): C08005 [DOI: 10.1029/2005JC003207http://dx.doi.org/10.1029/2005JC003207]
Wang J and Kang Y D. 1998. Study on population dynamics of phytoplankton in the Bohai sea. Marine Fisheries Research, 19(1): 43-52
王俊, 康元德. 1998. 渤海浮游植物种群动态的研究. 海洋水产研究, 19(1): 43-52
Wang J H. 2002. Phytoplankton communities in three distinct ecotypes of the Changjiang estuary. Periodical of Ocean University of China, 32(3): 422-428
王金辉. 2002. 长江口3个不同生态系的浮游植物群落. 青岛海洋大学学报, 32(3): 422-428 [DOI: 10.3969/j.issn.1672-5174.2002.03.013http://dx.doi.org/10.3969/j.issn.1672-5174.2002.03.013]
Wang L P, Zheng B H and Meng W. 2007. Response of two marine diatoms to change nitrogen and phosphorus concentrations in medium. Marine Environmental Science, 26(6): 546-549
王丽平, 郑丙辉, 孟伟. 2007. 两种海洋硅藻对生源要素N、P浓度变化的响应. 海洋环境科学, 26(6): 546-549 [DOI: 10.3969/j.issn.1007-6336.2007.06.012http://dx.doi.org/10.3969/j.issn.1007-6336.2007.06.012]
Woźniak B, Dera J, Ficek D, Majchrowski R, Kaczmarek S, Ostrowska M and Koblentz-Mishke O I. 1999. Modelling the influence of acclimation on the absorption properties of marine phytoplankton. Oceanologia, 41(2): 187-210
Woźniak B, Dera J, Ficek D, Majchrowski R, Kaczmarek S, Ostrowska M and Koblentz-Mishke O I. 2000. Model of the in vivo spectral absorption of algal pigments. Part 1. Mathematical apparatus. Oceanologia, 42(2): 177-190.
Wright S W, van den Enden R L, Pearce I, Davidson A T, Scott F J and Westwood K J. 2010. Phytoplankton community structure and stocks in the Southern Ocean (30-80°E) determined by CHEMTAX analysis of HPLC pigment signatures. Deep sea Research Part II: Topical Studies in Oceanography, 57(9/10): 758-778 [DOI: 10.1016/j.dsr2.2009.06.015http://dx.doi.org/10.1016/j.dsr2.2009.06.015]
Xi H Y, Hieronymi M, Röttgers R, Krasemann H and Qiu Z F. 2015. Hyperspectral differentiation of phytoplankton taxonomic groups: a comparison between using remote sensing reflectance and absorption spectra. Remote Sensing, 7(11): 14781-14805 [DOI: 10.3390/rs71114781http://dx.doi.org/10.3390/rs71114781]
Xi H Y, Losa S N, Mangin A, Soppa M A, Garnesson P, Demaria J, Liu Y Y, d'Andon O H F and Bracher A. 2020. Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data. Remote Sensing of Environment, 240: 111704 [DOI: 10.1016/j.rse.2020.111704http://dx.doi.org/10.1016/j.rse.2020.111704]
Xuitz J U, Huot Y, Bruyant F, Babin M and Claustre H. 2008. Relating phytoplankton photophysiological properties to community structure on large scales. Limnology and Oceanography, 53(2): 614-630 [DOI: 10.4319/lo.2008.53.2.0614http://dx.doi.org/10.4319/lo.2008.53.2.0614]
Ye H P, Zhang B, Liao X H, Li T J, Shen Q, Zhang F F, Zhu J H and Li J S. 2019. Gaussian decomposition and component pigment spectral analysis of phytoplankton absorption spectra. Journal of Oceanology and Limnology, 37(5): 1542-1554 [DOI: 10.1007/s00343-019-8079-zhttp://dx.doi.org/10.1007/s00343-019-8079-z]
Yuan M L, Sun J and Zhai W D. 2014. Phytoplankton community in Bohai Sea and the North Yellow Sea in Autumn 2012. Journal of Tianjin University of Science and Technology, 29(6): 56-64
苑明莉, 孙军, 翟惟东. 2014. 2012年秋季渤海和北黄海浮游植物群落. 天津科技大学学报, 29(6): 56-64 [DOI: 10.13364/j.issn.1672-6510.2014.06.012http://dx.doi.org/10.13364/j.issn.1672-6510.2014.06.012]
Zeidner G, Preston C M, Delong E F, Massana R, Post A F, Scanlan D J and Béjà O. 2003. Molecular diversity among marine picophytoplankton as revealed by psbA analyses. Environmental Microbiology, 5(3): 212-216 [DOI: 10.1046/j.1462-2920.2003.00403.xhttp://dx.doi.org/10.1046/j.1462-2920.2003.00403.x].
Zhan J, Zhang D J, Zhang G Y, Wang C X and Zhou G Q. 2020. Estimation of optical properties using QAA-V6 model based on MODIS data. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W10: 937-940 [10.5194/isprs-archives-XLII-3-W10-937-2020].
Zhang D. 2018. The Study of Phytoplankton and Biosilicon in the Yellow Sea and the Bohai Sea. Tianjin: Tianjin University of Science and Technology (张丹. 2018. 黄渤海浮游植物与生物硅的研究. 天津: 天津科技大学)
Zhang H L. 2019. Remote sensing of Phytoplankton Size Glasses and Phytoplankton Population Structures in the East China Sea. Nanjing: Nanjing University of Information Science and Technology (张海龙. 2019. 中国东海浮游植物粒级结构和种群结构的遥感研究. 南京: 南京信息工程大学) [DOI: 10.27248/d.cnki.gnjqc.2019.000726]
Zhang H L, Devred E, Fujiwara A, Qiu Z F and Liu X H. 2018. Estimation of phytoplankton taxonomic groups in the Arctic Ocean using phytoplankton absorption properties: implication for ocean-color remote sensing. Optics Express, 26(24): 32280-32301 [DOI: 10.1364/OE.26.032280http://dx.doi.org/10.1364/OE.26.032280]
Zhao Y, Yu R C, Kong F Z, Zhang Q C, Geng H X, Dai L, Wang J X and Zhou M J. 2019. Features of phytoplankton communities and their controlling factors in the yellow sea and the East China Sea in summer time. Oceanologia et Limnologia Sinica, 50(4): 838-850
赵越, 于仁成, 孔凡洲, 张清春, 耿慧霞, 代丽, 王锦秀, 周名江. 2019. 黄、东海夏季浮游植物群落特征及其影响因素分析. 海洋与湖沼, 50(4): 838-850 [DOI: 10.11693/hyhz20181100268http://dx.doi.org/10.11693/hyhz20181100268]
Zhu Q, Shen F, Shang P, Pan Y Q, Li M Y . Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning[J]. Remote Sensing, 2019, 11(17): 1-22 [DOI: 10.3390/rs11172001].
相关作者
相关机构