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

被引：0
作者：
Sun D. [1 ,2 ,3 ]
Chen Y. [1 ,3 ]
Liu J. [2 ,4 ]
Wang S. [1 ,2 ,3 ]
He Y. [1 ,2 ,3 ]
机构：
[1] School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing
[2] Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing
[3] Jiangsu Research Center for ocean Survey Technology, Nanjing
[4] National Satellite Ocean Application Service, Beijing
来源：
National Remote Sensing Bulletin | 2023年 / 27卷 / 01期
基金：
中国国家自然科学基金;
关键词：
CHEMTAX; HY-1C/D; Phytoplankton taxa concentrations; SVD; the Bohai Sea;
D O I：
10.11834/jrs.20221749
中图分类号：
学科分类号：
摘要：
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. © 2023 Science Press. All rights reserved.
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页码：128 / 144
页数：16
相关论文
共 88 条
  • [1] Aiken J, Pradhan Y, Barlow R, Lavender S, Poulton A, Holligan P, Hardman-Mountford N., 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, pp. 899-917, (2009)
  • [2] Balfoort H W, Snoek J, Smiths J R M, Breedveld L W, Hofstraat J W, Ringelberg J., Automatic identification of algae: neural network analysis of flow cytometric data, Journal of Plankton Research, 14, 4, pp. 575-589, (1992)
  • [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, Boss E S., Climate-driven trends in contemporary ocean productivity, Nature, 444, 7120, pp. 752-755, (2006)
  • [4] Boyce D G, Lewis M R, Worm B., Global phytoplankton decline over the past century, Nature, 466, 7306, pp. 591-596, (2010)
  • [5] Bracher A, Taylor M H, Taylor B, Dinter T, Rottgers R, Steinmetz F., Using empirical orthogonal functions derived from remote-sensing reflectance for the prediction of phytoplankton pigment concentrations, Ocean Science, 11, 1, pp. 139-158, (2015)
  • [6] Cai L N, Zhou M R, Liu J Q, Tang D L, Zuo J C., HY-1C observations of the impacts of islands on suspended sediment distribution in Zhoushan coastal waters, China, Remote Sensing, 12, 11, (2020)
  • [7] Chen G, Yang J, Zhang B T, Ma C Y., Thoughts and prospects on the new generation of marine science satellites, Periodical of Ocean University of China, 49, 10, pp. 110-117, (2019)
  • [8] Chen M., Summarization of Algorithms of Ocean Color Remote Sensing, (2005)
  • [9] Del Castillo C E, Coble P G., 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, pp. 1563-1579, (2000)
  • [10] Dupouy C, Benielli-Gary D, Neveux J, Dandonneau Y, Westberry T K., An algorithm for detecting Trichodesmium surface blooms in the South Western Tropical Pacific, Biogeosciences, 8, 12, pp. 3631-3647, (2011)
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