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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

Resümee

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.

Schlüsselwort

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

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