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Monthly average remote-sensing datasets of phytoplankton pigment concentrations in the Bohai Sea， Yellow Sea， and Eastern China Sea （1998—2020）

  • role： First author第一作者
  • Affiliation：

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

  • Email：sundeyong@nuist.edu.cn
  • Introduction：孙德勇，研究方向为海洋水色遥感。E-mail： sundeyong@nuist.edu.cn
SUN Deyong12,  
  • Affiliation：

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

LI Zhenghao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

  • Email：shengqiang.wang@nuist.edu.cn
  • Introduction：王胜强，研究方向为海洋生态遥感。E-mail： shengqiang.wang@nuist.edu.cn
WANG Shengqiang12*,  
  • Affiliation：

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

HUAN Yu1,  
  • Affiliation：

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

ZHANG Hailong12,  
  • Affiliation：

    College of Marine Science, University of South Florida, St. Petersburg FL33701, USA

QI Lin3,  
  • Affiliation：

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

    National Satellite Ocean Application Service, Beijing 100081, China

LIU Jianqiang24,  
  • Affiliation：

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

HE Yijun12

resumen

Studying marine phytoplankton communities is essential for understanding the carbon cycle and climate change. Phytoplankton pigments can describe the composition and physiological state of phytoplankton communities. Detecting phytoplankton pigment concentrations is also important, and remote sensing technology permits macroscopic long-term series monitoring of phytoplankton pigment concentrations. However, existing studies still have limitations. First, remote sensing methods for retrieving additional types of pigments are lacking. Existing studies have focused primarily on a few pigments or pigment groups. Second, the existing pigment inversion algorithms are mostly based on oceanic water data, and studies of optical class II waters off China are insufficient. Finally, satellite remote sensing datasets for long time series of multiple phytoplankton pigment concentrations in phytoplankton-related fields are lacking, indicating low data support. In this study, phytoplankton absorption data, 16 pigment concentration data points, and remote sensing reflectance data were collected. A total of 7 cruise experiments were performed in the Bohai Sea, Yellow Sea, and East China Sea from 2016 to 2018. Then, a remote-sensing model and a long-term series dataset of the spatiotemporal distribution of phytoplankton pigment concentrations were developed.The remote removal of fine particulate matter was achieved by determining the relationship between phytoplankton absorption and the 16 pigments. The measured absorption coefficients were decomposed into Gaussian functions, and the relationship between the Gaussian parameters and the measured pigment concentration was analyzed to construct inversion models. A two-component model of phytoplankton size classes was also used to determine hyperspectral phytoplankton absorption. The performance of the models was evaluated for consistency. Then, the models were assessed using in situ datasets and leave-one-out cross-validation methods. The results showed competitive and acceptable error results, with Mean Absolute Percentage Errors (MAPEs) of less than ~60% for most pigments. Satellite-measured validation also produced promising prediction errors, yielding MAPEs in the range of 40%—60% for most pigments. Finally, the developed models were applied to the SeaWiFS and MODIS-Aqua remote sensing reflectance monthly mean products (1998—2020) to obtain 23 years of spatiotemporal patterns of 16 pigment concentrations in the Bohai Sea, Yellow Sea, and East China Sea.The satellite remote sensing dataset revealed 16 similar pigment distribution patterns, revealing a decreasing trend from nearshore to offshore waters. In the Bohai Sea, the pigment concentration is high in winter and spring and low in summer. In summer, the pigment concentration peaks in the coastal areas of Jiangsu Province and gradually decreases toward Zhejiang and Fujian Provinces. A triangular high concentration is apparent in the Yangtze River Estuary, with the area extending from west to east in autumn and winter. The phytoplankton pigment concentration was relatively low in the outer deepwater area, and the variation in concentration with season was only slight.The remote sensing datasets of 16 phytoplankton pigment concentrations can be downloaded fromhttps://doi.org/10.17632/bhcznf2m7v.1. In related fields, scholars can study the macroscopic and continuous phytoplankton community structure monitoring and physiological characteristics of phytoplankton in the Bohai Sea, Yellow Sea, and East China Sea based on information from pigment concentration remote sensing datasets. This dataset can enrich the understanding of marine phytoplankton pigment distributions and provide data support for satellite-based detection of phytoplankton community composition.

palabra clave

Phytoplankton pigments;absorption coefficient;Coastal water;SeaWiFS;MODIS;Remote sensing dataset

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    Brewin R J W, Sathyendranath S, Jackson T, Barlow R, Brotas V, Airs R and Lamont T. 2015. Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class. Remote Sensing of Environment, 168: 437-450
  4. 4.
    Catlett D and Siegel D A. 2018. Phytoplankton pigment communities can be modeled using unique relationships with spectral absorption signatures in a dynamic coastal environment. Journal of Geophysical Research: Oceans, 123(1): 246-264
  5. 5.
    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
  6. 6.
    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: Oceans, 122(12): 9725-9743
  7. 7.
    Cleveland J S and Weidemann A D. 1993. Quantifying absorption by aquatic particles: a multiple scattering correction for glass-fiber filters. Limnology and Oceanography, 38(6): 1321-1327
  8. 8.
    Devred E, Sathyendranath S, Stuart V, Maass H, Ulloa O and Platt T. 2006. A two-component model of phytoplankton absorption in the open ocean: theory and applications. Journal of Geophysical Research: Oceans, 111(C3): C03011
  9. 9.
    Falkowski P G, Barber R T and Smetacek V. 1998. Biogeochemical controls and feedbacks on ocean primary production. Science, 281(5374): 200-206
  10. 10.
    Harding L W, Mallonee M E, Perry E S, Miller W D, Adolf J E, Gallegos C L and Paerl H W. 2016. Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay. Scientific Reports, 6: 23773
  11. 11.
    Hirata T, Hardman-Mountford N J, Brewin R J W, Aiken J, Barlow R, Suzuki K, Isada T, Howell E, Hashioka T, Noguchi-Aita M and Yamanaka Y. 2011. Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types. Biogeosciences, 8(2): 311-327
  12. 12.
    Hoepffner N and Sathyendranath S. 1993. Determination of the major groups of phytoplankton pigments from the absorption spectra of total particulate matter. Journal of Geophysical Research: Oceans, 98(C12): 22789-22803
  13. 13.
    Huan Y, Sun D Y, Wang S Q, Zhang H L, Qiu Z F, Bilal M and He Y J. 2021. Remote sensing estimation of phytoplankton absorption associated with size classes in coastal waters. Ecological Indicators, 121: 107198
  14. 14.
    IOCCG. 2014. Phytoplankton functional types from space. Reports of the international ocean color coordinating group (No. 15). Dartmouth: IOCCG
  15. 15.
    Jeffrey S W, Wright S W and Zapata M. 2011. Microalgal classes and their signature pigments//Roy S, Llewellyn C A, Egeland E S and Johnsen G, eds. Phytoplankton Pigments: Characterization, Chemotaxonomy and Applications in Oceanography. Cambridge: Cambridge University Press: 3-77
  16. 16.
    Kramer S J and Siegel D A. 2019. How can phytoplankton pigments be best used to characterize surface ocean phytoplankton groups for ocean color remote sensing algorithms?. Journal of Geophysical Research: Oceans, 124(11): 7557-7574
  17. 17.
    Le Quéré C, Harrison S P, Prentice I C, Buitenhuis E T, Aumont O, Bopp L, Claustre H, Da Cunha L C, Geider R, Giraud X, Klaas C, Kohfeld K E, Legendre L, Manizza M, Platt T, Rivkin R B, Sathyendranath S, Uitz J, Watson A J and Wolf-Gladrow D. 2005. Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models. Global Change Biology, 11(11): 2016-2040
  18. 18.
    Li Z H, Chen Z Z, Wang L Y, Sun D Y, Zhao B R and Wang S Q. 2021. Remote sensing inversion of concentration of phytoplankton chlorophyll and carotenoid from GOCI measurements in coastal waters. Acta Optica Sinica, 41(2): 0201001
  19. 19.
    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
  20. 20.
    Pan X J, Mannino A, Russ M E, Hooker S B and Harding L W. 2010. Remote sensing of phytoplankton pigment distribution in the United States northeast coast. Remote Sensing of Environment, 114(11): 2403-2416
  21. 21.
    Pan X J, Wong G T F, Ho T Y, Shiah F K and Liu H B. 2013. Remote sensing of picophytoplankton distribution in the northern South China Sea. Remote Sensing of Environment, 128: 162-175
  22. 22.
    Sieburth J M, Smetacek V and Lenz J. 1978. Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnology and Oceanography, 23(6): 1256-1263
  23. 23.
    Siegel D A, Buesseler K O, Doney S C, Sailley S F, Behrenfeld M J and Boyd P W. 2014. Global assessment of ocean carbon export by combining satellite observations and food-web models. Global Biogeochemical Cycles, 28(3): 181-196
  24. 24.
    Sun D Y, Huan Y, Wang S Q, Qiu Z F, Ling Z B, Mao Z H and He Y J. 2019. Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and East China Sea. Water Research, 157: 119-133
  25. 25.
    Sun D Y, Pan T F, Wang S Q and Hu C M. 2021. Linking phytoplankton absorption to community composition in Chinese marginal seas. Progress in Oceanography, 192: 102517
  26. 26.
    Swan C M, Vogt M, Gruber N and Laufkoetter C. 2016. A global seasonal surface ocean climatology of phytoplankton types based on CHEMTAX analysis of HPLC pigments. Deep Sea Research Part I: Oceanographic Research Papers, 109: 137-156
  27. 27.
    Van Heukelem L and Thomas C S. 2001. Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments. Journal of Chromatography A, 910(1): 31-49
  28. 28.
    Vidussi F, Claustre H, Manca B B, Luchetta A and Marty J C. 2001. Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter. Journal of Geophysical Research: Oceans, 106(C9): 19939-19956
  29. 29.
    Wang G F, Zhang Y X, Xu W L, Zhou W, Wu H L, Xu Z T and Cao W X. 2021. Estimation of phytoplankton pigment concentration in the South China Sea from hyperspectral absorption data. Acta Optica Sinica, 41(6): 0601002
  30. 30.
    Wang G Q, Lee Z and Mouw C B. 2018. Concentrations of multiple phytoplankton pigments in the global oceans obtained from satellite ocean color measurements with MERIS. Applied Sciences, 8(12): 2678

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