سابِق|تال

Remote sensing estimation of phytoplankton groups using Chinese ocean Color satellite data

  • 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

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

  • Email：sundeyong@nuist.edu.cn
  • Introduction：孙德勇，研究方向为水光学和水色遥感领域的教学和科研工作。E-mail：sundeyong@nuist.edu.cn
SUN Deyong123,  
  • Affiliation：

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

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

CHEN Yuhang13,  
  • role： Corresponding author通信作者
  • 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

  • Email：jqliu@mail.nsoas.org.cn
  • Introduction：刘建强，研究方向为海洋卫星工程建设、海洋遥感应用及规划论证。E-mail：jqliu@mail.nsoas.org.cn
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

    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

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

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

HE Yijun123

ملخص

Phytoplankton is a significant producer of global primary productivity and influences the ocean’s biological cycle and energy conversion. Understanding and detecting the phytoplankton biomass is important to grasp the variations in the marine environment. However, observing the changes of phytoplankton taxa remains a great challenge on spatial and temporal scales. Recent developments in ocean color sensors have enabled large-scale and long time-series remote sensing retrieval of phytoplankton biomass. HaiYang-1C and HaiYang-1D (HY-1C/D) satellites, as the main members of the Chinese ocean color satellite series, can provide ocean color products with a larger observation range, higher accuracy, and resolution, with great application potential.In this study, we collect in situ data, including the pigment concentration with the high-performance liquid chromatography method (HPLC) and remote sensing reflectance (Rrs), from four cruises in the Bohai Sea and the Yellow Sea from 2016 to 2018. Then, we obtain eight typical phytoplankton taxa concentrations through CHEMTAX (CHEMical TAXonomy) software based on these pigment data. We found the sum of the relative contributions of diatoms, cryptophytes, cyanobacteria, and chlorophytes to total chlorophyll a (TChla) accounted for a large proportion (79%). In addition, the spatial distribution of the CHEMTAX-calculated phytoplankton taxa showed a trend of higher nearshore concentration than offshore by spatial interpolation analysis.We used the singular value decomposition (SVD) method to construct a link between Rrs and phytoplankton concentrations. The matrix U obtained from SVD was used to build four models by multiple linear regression methods, to estimate four phytoplankton taxa concentrations. We carried out validation independently based on the measured and estimated concentrations, and the result showed relatively high consistent between diatoms, cryptophytes, cyanobacteria, and chlorophytes and the measured values (determination coefficients (R2): 0.44, 0.70, 0.70 and 0.71 (p<0.001); median percent error (ME): 44.81%, 45.34%, 51.20% and 62.80%; Root Mean Squared Error (RMSE): 0.23 mg/m3, 0.24 mg/m3, 0.11 mg/m3 and 0.06 mg/m3, respectively). The established model was further applied to China Ocean Color & Temperature Scanner (COCTS) Rrs data on the HY-1C/D L1A to demonstrate the spatial distribution of four major phytoplankton taxa in the Bohai Sea. The satellite results are consistent with previous studies that showed decreasing concentrations from nearshore to offshore.Finally, this study applies the same modeling approach (SVD) to MODIS and GOCI sensor bands. A comparison of model performance and satellite applications between the three sensors showed that the new model established by COCTS bands outperformed the GOCI-Ⅱ model and was similar to the MODIS-Aqua model. Also, the satellite application of COCTS is superior to the other two sensors. Generally, this study can provide a methodological foundation for understanding the spatial-temporal evolution of the phytoplankton community in the Bohai Sea. Meanwhile, this study shows the great potential of HY-1C/D in models establishing and phytoplankton community monitoring.

مفهوم

Phytoplankton taxa concentrations;CHEMTAX;SVD;HY-1C/D;The Bohai Sea

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 PartⅡ: Topical Studies in Oceanography, 56(15): 899-917
  2. 2.
    Balfoort H W, Snoek J, Smiths J R M, Breedveld L W, Hofstraat J W and Ringelberg J. 1992. Automatic identification of algae: neural network analysis of flow cytometric data. Journal of Plankton Research, 14(4): 575-589
  3. 3.
    Behrenfeld M J, O’Malley R T, Siegel D A, McClain C R, Sarmiento J L, Feldman G C, Milligan A J, Falkowski P G, Letelier R M and Boss E S. 2006. Climate-driven trends in contemporary ocean productivity. Nature, 444(7120): 752-755
  4. 4.
    Boyce D G, Lewis M R and Worm B. 2010. Global phytoplankton decline over the past century. Nature, 466(7306): 591-596
  5. 5.
    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
  6. 6.
    Cai L N, Zhou M R, Liu J Q, Tang D L and Zuo J C. 2020. HY-1C observations of the impacts of islands on suspended sediment distribution in Zhoushan coastal waters, China. Remote Sensing, 12(11): 1766
  7. 7.
    Chen G, Yang J, Zhang B T and Ma C Y. 2019. Thoughts and prospects on the new generation of marine science satellites. Periodical of Ocean University of China, 49(10): 110-117
  8. 8.
    Chen M. 2005. Summarization of Algorithms of Ocean Color Remote Sensing. Qingdao: Ocean University of China
  9. 9.
    Del Castillo C E and Coble P G. 2000. Seasonal variability of the colored dissolved organic matter during the 1994—95 NE and SW Monsoons in the Arabian Sea. Deep Sea Research Part Ⅱ: Topical Studies in Oceanography, 47(7/8): 1563-1579
  10. 10.
    Dupouy C, Benielli-Gary D, Neveux J, Dandonneau Y and Westberry T K. 2011. An algorithm for detecting Trichodesmium surface blooms in the South Western Tropical Pacific. Biogeosciences, 8(12): 3631-3647
  11. 11.
    Furuya K, Hayashi M, Yabushita Y and Ishikawa I. 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
  12. 12.
    Gong C L and Fan W. 2002. Algorithms for case 2 waters of remote sensing of ocean color. Marine Science Bulletin, 21(2): 77-83
  13. 13.
    He M X, Liu J P, Yu J, Li D Q and Hu C M. 2011. Monitoring green tides in Chinese marginal seas//Morales J, Stuart V, Platt T and Sathyendranath S eds. Handbook of Satellite Remote Sensing Image Interpretation: Applications for Marine Living Resources Conservation and Management. Dartmouth: EU PRESPO and IOCCG: 111-124
  14. 14.
    Hooker S B, Firestone E R and Lazin G. 2000. Sea WiFS postlaunch technical report series. Volume 8; The SeaBOARR-99 field campaign. Ceamos Cl, 55(5): 2827-2832 []
  15. 15.
    Hu C M. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113(10): 2118-2129
  16. 16.
    Hu C M, Cannizzaro J, Carder K L, Muller-Karger F E and Hardy R. 2010a. Remote detection of Trichodesmium blooms in optically complex coastal waters: examples with MODIS full-spectral data. Remote Sensing of Environment, 114(9): 2048-2058
  17. 17.
    Hu C M, Li D Q, Chen C S, Ge J Z, Muller-Karger F E, Liu J P, Yu F and He M X. 2010b. On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea. Journal of Geophysical Research: Oceans, 115(C5): C05017
  18. 18.
    Hu L B, Hu C M and He M X. 2017. Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea. Remote Sensing of Environment, 192: 217-227
  19. 19.
    Jeffrey S W and Hallegraeff G M. 1987. Phytoplankton pigments, species and light climate in a complex warm-core eddy of the East Australian Current. Deep Sea Research, 34(5): 649-673
  20. 20.
    Jiang D J and Zhang H. 2018. Analysis of spatial and temporal characteristics of chlorophyll-a concentration and red tide monitoring in Bohai Sea. Marine Sciences, 42(5): 23-31
  21. 21.
    Jiang X W, He X Q, Lin M S, Gong F, Ye X M and Pan D L. 2019. Progresses on ocean satellite remote sensing application in China. Haiyang Xuebao, 41(10): 113-124
  22. 22.
    Jiang X W, Lin M S and Zhang Y G. 2016. Progress and prospect of Chinese ocean satellites. Journal of Remote Sensing, 20(5): 1185-1198
  23. 23.
    Jiao N Z and Yang Y H. 1999. Simultaneous monitoring of autotrophic picoplankton and heterotrophic bacteria. Oceanologia et Limnologia Sinica, 30(5): 506-511
  24. 24.
    Jin D X. 1991. Marine diatomology. Xiamen: Xiamen University Press
  25. 25.
    Lee Z, Ahn Y H, Mobley C and Arnone R. 2010. Removal of surface-reflected light for the measurement of remote-sensing reflectance from an above-surface platform. Optics Express, 18(25): 26313-26324
  26. 26.
    Li W K and Dickie P M. 2001. Monitoring phytoplankton, bacterioplankton, and virioplankton in a coastal inlet (Bedford Basin) by flow cytometry. Cytometry, 44(3): 236-246
  27. 27.
    Liang C, Liu L, Liu J Q, Zou B, Zou Y R and Cui S X. 2020. Extracting mangrove information using MNF transformation based on HY-1C CZI spectral indices reconstruction data. Haiyang Xuebao, 42(4): 104-112
  28. 28.
    Lin J F, Cao W X, Wang G F and Hu S B. 2014. Satellite-observed variability of phytoplankton size classes associated with a cold eddy in the South China Sea. Marine Pollution Bulletin, 83(1): 190-197
  29. 29.
    Lin M S, He X Q, Jia Y J, Bai Y, Ye X M and Gong F. 2019. Advances in marine satellite remote sensing technology in China. Haiyang Xuebao, 41(10): 99-112
  30. 30.
    Lin M S, Zhang Y G and Yuan X Z. 2015. The Developmet course and trend of ocean remote sensing satellite. Haiyang Xuebao, 37(1): 1-10
  31. 31.
    Liu H R, Liu X, Xiao W P, Laws E A and Huang B Q. 2021. Spatial and temporal variations of satellite-derived phytoplankton size classes using a three-component model bridged with temperature in Marginal Seas of the Western Pacific Ocean. Progress in Oceanography, 191: 102511-102529
  32. 32.
    Liu J Q, Jiang X W and Lin M S. 2012. Analysis of problems and countermeasures in the development of Chinese ocean satellite. Satellite Application, (2): 54-60
  33. 33.
    Liu J Q, Zeng T, Liang C, Zou Y R, Ye X M, Ding J, Zou B, Shi L J and GUO M H. 2020. Application of HY-1C in natural disaster monitoring. Satellite Application, (6): 26-34
  34. 34.
    Liu J Q, Zeng T, Ye X M, Liu J P and Ma X F. 2021. Monitoring of ice crack changes and fracture processes on the Antarctic Brent Ice Shelf by HY-1C/D. Haiyang Xuebao, 43(7): 205-206
  35. 35.
    Liu S X, Fan J F and Wang Z L. 2013. The seasonal changes of phytoplankton community in North Yellow Sea, China. Ecology and Environmental Sciences, 22(7): 1173-1181
  36. 36.
    Liu X, Huang B Q, Huang Q, Wang L, Ni X B, Tang Q S, Sun S, Wei H, Liu S M, Li C L and Sun J. 2015. Seasonal phytoplankton response to physical processes in the southern Yellow Sea. Journal of Sea Research, 95: 45-55
  37. 37.
    Luan Q S, Kang Y D and Wang J. 2018. Long-term changes in the phytoplankton community in the Bohai Sea (1959—2015). Progress in Fishery Sciences, 39(4): 9-18
  38. 38.
    Martinez E, Antoine D, D’Ortenzio F and Gentili B. 2009. Climate-driven basin-scale decadal oscillations of oceanic phytoplankton. Science, 326(5957): 1253-1256
  39. 39.
    McKinna L I W, Furnas M J and Ridd P V. 2011. A simple, binary classification algorithm for the detection of Trichodesmium spp. within the Great Barrier Reef using MODIS imagery. Limnology and Oceanography: Methods, 9(2): 50-66
  40. 40.
    Mobley C D. 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36): 7442-7455
  41. 41.
    Moore T S, Dowell M D and Franz B A. 2012. Detection of coccolithophore blooms in ocean color satellite imagery: a generalized approach for use with multiple sensors. Remote Sensing of Environment, 117: 249-263
  42. 42.
    Pan D L and Bai Y. 2008. Advances on the application of ocean color remote sensing engineering in China. Engineering Sciences, 10(9): 14-24, 46
  43. 43.
    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
  44. 44.
    Qi L, Hu C M, Wang M Q, Shang S L and Wilson C. 2017. Floating algae blooms in the East China Sea. Geophysical Research Letters, 44(22): 11501-11509
  45. 45.
    Qi L, Hu C M, Xing Q G and Shang S L. 2016. Long-term trend of Ulva prolifera blooms in the western Yellow Sea. Harmful Algae, 58: 35-44
  46. 46.
    Shen Y F, Liu J Q, Ding J, Jiao J N, Sun S J and Lu Y C. 2020. HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. Journal of Remote Sensing, 24(8): 933-944
  47. 47.
    Shutler J D, Grant M G, Miller P I, Rushton E and Anderson K. 2010. Coccolithophore bloom detection in the north east Atlantic using SeaWiFS: algorithm description, application and sensitivity analysis. Remote Sensing of Environment, 114(5): 1008-1016
  48. 48.
    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
  49. 49.
    Song K S, Li L, Tedesco L P, Li S, Hall B E and Du J. 2014. Remote quantification of phycocyanin in potable water sources through an adaptive model. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 68-80
  50. 50.
    Stevenson R J, Peterson C G, Kirschtel D B, King C C and Tuchman N C. 1991. Succession and ecological strategies of benthic diatoms (Bacillariophyceae): density-dependent growth and effects of nutrients and shading. Journal of Phycology, 27: 59-69
  51. 51.
    Su J L. 2005. China Offshore Hydrology. Beijing: China Ocean Press: 174-181
  52. 52.
    Suikkanen S, Laamanen M and Huttunen M. 2007. Long-term changes in summer phytoplankton communities of the open northern Baltic Sea. Estuarine, Coastal and Shelf Science, 71(3/4): 580-592
  53. 53.
    Sun D Y, Fan J, Wang S Q, Zhang H L, Shen X J, Yuan Y B and He Y J. 2021. Understanding optical absorption associated with phytoplanktonic groups in the marginal seas. Science of the Total Environment, 789: 147846-147859
  54. 54.
    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: Oceans, 122(10): 8309-8325
  55. 55.
    Sun D Y, Lai W D, Wang S Q, Huan Y, Bilal M, Qiu Z F and He Y J. 2019. Synoptic relationships to estimate phytoplankton communities specific to sizes and species from satellite observations in coastal waters. Optics Express, 27(16): A1156-A1172
  56. 56.
    Sun D Y, Li Y M, Wang Q, Lu H, Le C F, Huang C C and Gong S Q. 2011. A neural-network model to retrieve CDOM absorption from in situ measured hyperspectral data in an optically complex lake: Lake Taihu case study. International Journal of Remote Sensing, 32(14): 4005-4022
  57. 57.
    Suzuki K, Hinuma A, Saito H, Kiyosawa H, Liu H B, Saino T and Tsuda A. 2005. Responses of phytoplankton and heterotrophic bacteria in the northwest subarctic Pacific to in situ iron fertilization as estimated by HPLC pigment analysis and flow cytometry. Progress in Oceanography, 64(2/4): 167-187
  58. 58.
    Tang J W, Tian G L, Wang X Y, Wang X M and Song Q J. 2004. The methods of water spectra measurement and analysis I: above-water method. Journal of Remote Sensing, 8(1): 37-44
  59. 59.
    Tao B Y, Mao Z H, Lei H, Pan D L, Bai Y, Zhu Q K and Zhang Z L. 2017. A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea. Journal of Geophysical Research: Oceans, 122(3): 1772-1788
  60. 60.
    Tao B Y, Mao Z H, Lei H, Pan D L, Shen Y Z, Bai Y, Zhu Q K and Li Z E. 2015. A novel method for discriminating Prorocentrum donghaiense from diatom blooms in the East China Sea using MODIS measurements. Remote Sensing of Environment, 158: 267-280
  61. 61.
    Taylor B B, Taylor M H, Dinter T and Bracher A. 2013. Estimation of relative phycoerythrin concentrations from hyperspectral underwater radiance measurements—A statistical approach. Journal of Geophysical Research: Oceans, 118(6): 2948-2960
  62. 62.
    Tomas C R. 1997. Identifying Marine Phytoplankton. San Diego: Academic Press: 1-858
  63. 63.
    Utermöhl H. 1958. Zur vervollkommnung der quantitativen phytoplankton-methodik. SIL Communications, 1953—1996, 9(1): 1-38
  64. 64.
    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
  65. 65.
    Wang H L, Hong H S and Xu L. 1999. Separation and determination of chlorophylls and carotenoids from marine phytoplankton by RP-HPLC. Marine Sciences, (4): 6-9
  66. 66.
    Wen Z B, Wu Y T, Li C and Yin J P. 2021. The current situation and consideration of the application and development of marine satellite data in China. Journal of Tropical Oceanography, 40(6): 23-30
  67. 67.
    Wen Z D, Song K S, Zhao Y, Du J and Ma J H. 2016. Influence of environmental factors on spectral characteristics of chromophoric dissolved organic matter (CDOM) in Inner Mongolia Plateau, China. Hydrology and Earth System Sciences, 20(2): 787-801
  68. 68.
    Xi H Y, Losa S N, Mangin A, Garnesson P, Bretagnon M, Demaria J, Soppa M A, d’Andon O H F and Bracher A. 2021. Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multisensor ocean color and sea surface temperature satellite products. Journal of Geophysical Research: Oceans, 126(5): e2020JC017127
  69. 69.
    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: 11704
  70. 70.
    Xiao C, Sun D Y, Wang S Q, Qiu Z F, Huan Y and Zhang J B. 2018. Long-term changes in colored dissolved organic matter from satellite observations in the Bohai Sea and North Yellow Sea. Remote Sensing, 10(5): 688
  71. 71.
    Xu S G, Fu Y Z and Kang P P. 2015. Seasonal and interannual variations of Chlorophyll a in Bohai Sea. Marine Environmental Science, 34(6): 898-903, 924
  72. 72.
    Xu X Y. 2019. Deviation Analysis on GOCI, VIIRS and MODIS Remote Sensing Reflectance Products in China Offshore Area. Hangzhou: Hangzhou Normal University
  73. 73.
    Yang B H. 2011. Constructing China’s Ocean Satellite System to Enhance the Capability of Ocean Environment and Disaster Monitoring. Chinese Space Science and Technology, 31(5): 1-8
  74. 74.
    Yang W, Zhou N and Cao J S. 2021. Double-camera geometric stitching of HY-1D coastal zone imager. Spacecraft Recovery and Remote Sensing, 42(4): 99-107
  75. 75.
    Yao L J, Cao W X, Wang G F, Xu Z T, Hu S B, Zhou W and Li C. 2015. A support vector machine model to estimate phytoplankton size classes. Journal of Tropical Oceanography, 34(4): 37-47
  76. 76.
    Yao P, Yu Z G and Mi T Z. 2003. The chemotaxonomy of marine phytoplankton. Marine Environmental Science, 22(1): 75-80
  77. 77.
    Zeidner G, Preston C M, Delong E F, Massana R, Post A F, Scanlan D J, Béjà O. 2003. Molecular diversity among marine picophytoplankton as revealed by psbA analyses. Environmental Microbiology, 5(3):212-6
  78. 78.
    Zhai H C, Ning X R, Tang X X and Zhou W L. 2010. Application of HPLC Pigment Analysis in the Study of Phytoplankton Community Structure. Periodical Of Ocean University Of China, 40(11): 71-78
  79. 79.
    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
  80. 80.
    Zhang K L, Ding Z Y and Gao J. 2019a. Design and application of AIS on HY-1C/D satellite. Spacecraft Engineering, 28(5): 26-31
  81. 81.
    Zhang K L, Zhang Y C and Ma Y. 2019b. Design of on orbit cross calibration method for HY-1C/D satellite. Spacecraft Engineering, 28(2): 24-29
  82. 82.
    Zhang L H, Zhang J and Chao M. 2002. Identification of Microphytoplankton populations in seawater by flow cytometry. Marine Sciences, 26(3): 60-65
  83. 83.
    Zhao C Y, Zang J Y, Liu J, Sun T and Ran X B. 2016. Distribution and budget of nitrogen and phosphorus and their influence on the ecosystem in the Bohai Sea and Yellow Sea. China Environmental Science, 36(7): 2115-2127
  84. 84.
    Zhou Y L, Zhang C S, Shi X Y and Su R G. 2017. Distribution characteristics of chlorophyll a and its influencing environmental factors in Bohai Sea and Yellow Sea. China Environmental Science, 37(11): 4259-4265
  85. 85.
    Zhu Z Y, Ng W M, Liu S M, Zhang J, Chen J C and Wu Y. 2009. Estuarine phytoplankton dynamics and shift of limiting factors: a study in the Changjiang (Yangtze River) Estuary and adjacent area. Estuarine, Coastal and Shelf Science, 84(3): 393-401
  86. 86.
    Zou Y R, Liu J Q, Liang C and Zhu H T. 2020. Monitoring of mangrove growth using HY-1C Satellite CZI data based on remote sensing. Journal of Marine Sciences, 38(1): 68-76
  87. 87.
    Zou Y R. 2004. Analysis spatial distribution characteristics for chla in Bohai Sea. Remote Sensing Information, (3): 30-31

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