Früher|Nächster

Inversion model of diatom concentrations in coastal water based on absorption coefficients

  • role： First author第一作者
  • Affiliation：

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

  • Email：2978198775@qq.com
  • Introduction：樊杰，研究方向为海洋遥感。E-mail：2978198775@qq.com
FAN Jie1,  
  • role： Corresponding author通信作者
  • Affiliation：

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

    Jiangsu Key Laboratory of Ocean Dynamics Remote Sensing and Acoustics, Nanjing 210044, China

    Jiangsu Research Center for ocean Survey Technology, Nanjing 210044, China

  • Email：sundeyong@nuist.edu.cn
  • Introduction：孙德勇，研究方向为水体光学、水色遥感。E-mail：sundeyong@nuist.edu.cn
SUN Deyong123*,  
  • Affiliation：

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

    Jiangsu Key Laboratory of Ocean Dynamics Remote Sensing and Acoustics, Nanjing 210044, China

    Jiangsu Research Center for ocean Survey Technology, Nanjing 210044, China

WANG Shengqiang123,  
  • Affiliation：

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

    Jiangsu Key Laboratory of Ocean Dynamics Remote Sensing and Acoustics, Nanjing 210044, China

    Jiangsu Research Center for ocean Survey Technology, Nanjing 210044, China

ZHANG Hailong123,  
  • 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

    Jiangsu Key Laboratory of Ocean Dynamics Remote Sensing and Acoustics, Nanjing 210044, China

    Jiangsu Research Center for ocean Survey Technology, Nanjing 210044, China

HE Yijun123

Resümee

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, R2, 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 (R2) 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.

Schlüsselwort

remote sensing;Diatoms concentration;Gaussian model;CHEMTAX;Chinese marginal seas

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.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Falkowski P G, Barber R T and Smetacek V. 1998. Biogeochemical controls and feedbacks on ocean primary production. Science, 281(5374): 200-206
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    Hoepffner N and Sathyendranath S. 1991. Effect of pigment composition on absorption properties of phytoplankton. Marine Ecology Progress Series, 73: 11-23
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    Lee Z P. 2006. Remote Sensing of Inherent Optical Properties: Fundamentals, Tests of Algorithms, and Applications. Dartmouth: International Ocean Colour Coordinating Group (IOCCG)
  28. 28.
    Li J and Li C L. 2004. Deleterious effects of diatom in high concentration on copepod reproduction. Acta Ecologica Sinica, 24(11): 2664-2670
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    Lin P M. 2008. Study on Distribution and Speciation of Silica in Tonkin Gulf. Xiamen: Xiamen University
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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
  45. 45.
    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
  46. 46.
    Su J L. 2001. A review of circulation dynamics of the coastal oceans near China. Acta Oceanologica Sinica, 23(4): 1-16
  47. 47.
    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
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    Wang J and Kang Y D. 1998. Study on population dynamics of phytoplankton in the Bohai sea. Marine Fisheries Research, 19(1): 43-52
  52. 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
  53. 53.
    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
  54. 54.
    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
  55. 55.
    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.
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    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
  60. 60.
    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
  61. 61.
    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
  62. 62.
    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 .
  63. 63.
    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].
  64. 64.
    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)
  65. 65.
    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]
  66. 66.
    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
  67. 67.
    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
  68. 68.
    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].

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website