1998年—2020年渤海、黄海和东海月平均典型浮游植物色素浓度遥感数据集  

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

作　　者：孙德勇 李正浩 王胜强 环宇 张海龙 齐琳 刘建强 何宜军[1,2] SUN Deyong;LI Zhenghao;WANG Shengqiang;HUAN Yu;ZHANG Hailong;QI Lin;LIU Jianqiang;HE Yijun(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;College of Marine Science,University of South Florida,St.Petersburg FL33701,USA;National Satellite Ocean Application Service,Beijing 100081,China)

机构地区：[1]南京信息工程大学海洋科学学院,南京210044 [2]自然资源部空间海洋遥感与应用重点实验室,北京100081 [3]美国南佛罗里达大学海洋科学学院,圣彼德斯堡FL33701 [4]国家卫星海洋应用中心,北京100081

出　　处：《遥感学报》2024年第4期1101-1111,共11页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42176179,42176181,42106176,4207060172)。

摘　　要：海洋浮游植物群落结构信息对了解和研究碳循环和气候变化至关重要。浮游植物中含有的多种色素能够用于描述浮游植物群落结构特征和生理状态,因此浮游植物色素浓度的检测具有重要意义。本研究基于2016年—2018年渤海、黄海和东海7次航次调查实验中采集的实测数据(包括浮游植物吸收系数、16种典型色素浓度和遥感反射率),利用高斯分解法开发了一种基于浮游植物吸收的色素浓度遥感模型,并使用原位观测数据集进行评估,得到的结果的误差可接受(例如,大部分色素模型的平均绝对百分比误差MAPE<60%)。卫星匹配验证也表明反演结果与实测数据的一致性(大部分色素模型的MAPE范围40%—60%)。将本文开发的模型应用于SeaWiFS和MODIS-Aqua遥感反射率月平均产品(1998年—2020年),获得了渤海、黄海和东海区域16种色素浓度23年的时空变化数据记录。浮游植物色素浓度遥感数据集可以从https://doi.org/10.17632/bhcznf2m7v.1下载获得。本研究从遥感反射率卫星数据中得到复杂浑浊沿海水域中的浮游植物色素浓度,可为渤海、黄海和东海精细化的海洋浮游植物群落结构研究提供数据支撑。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 Pe

关 键 词：浮游植物色素 吸收系数 沿海水域 SEAWIFS MODIS 遥感数据集 

分 类 号：P733.3[天文地球—物理海洋学] P2[天文地球—海洋科学]