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中国东部海域浮游植物类群遥感反演研究

赵海阳 沈芳 孙雪融 魏小岛

赵海阳,沈芳,孙雪融,等. 中国东部海域浮游植物类群遥感反演研究[J]. 海洋学报,2022,44(4):153–168 doi: 10.12284/hyxb2022062
引用本文: 赵海阳,沈芳,孙雪融,等. 中国东部海域浮游植物类群遥感反演研究[J]. 海洋学报,2022,44(4):153–168 doi: 10.12284/hyxb2022062
Zhao Haiyang,Shen Fang,Sun Xuerong, et al. Remote sensing retrieval of phytoplankton group in the eastern China seas[J]. Haiyang Xuebao,2022, 44(4):153–168 doi: 10.12284/hyxb2022062
Citation: Zhao Haiyang,Shen Fang,Sun Xuerong, et al. Remote sensing retrieval of phytoplankton group in the eastern China seas[J]. Haiyang Xuebao,2022, 44(4):153–168 doi: 10.12284/hyxb2022062

中国东部海域浮游植物类群遥感反演研究

doi: 10.12284/hyxb2022062
基金项目: 国家自然科学基金(42076187,41771378)。
详细信息
    作者简介:

    赵海阳(1997-),男,河南省许昌市人,主要从事海洋水色遥感研究。E-mail: 51193904013@stu.ecnu.edu.cn

    通讯作者:

    沈芳,女,教授,主要从事海洋水色遥感研究。E-mail: fshen@sklec.ecnu.edu.cn

  • 中图分类号: TP79

Remote sensing retrieval of phytoplankton group in the eastern China seas

  • 摘要: 浮游植物类群遥感反演能够为全面认识浮游植物在海洋生态系统中的作用提供重要的数据资料。但由于复杂的水体光学特性,近海浮游植物类群遥感反演存在着巨大挑战。本研究以复杂光学二类水体—中国东部海域为研究区,通过使用3种建模方法,即波段组合法、基于奇异值分解的多元线性回归法、基于奇异值分解的XGBoost回归法,利用遥感反射率数据反演浮游植物类群。经原位实测数据集验证,基于奇异值分解的XGBoost回归法构建的8类浮游植物叶绿素a浓度反演模型的精度最高,其中硅藻、甲藻的叶绿素a浓度反演模型在验证集上的决定系数均大于0.7。相比之下,3种建模方法估算得到的绿藻、蓝藻和金藻的叶绿素a浓度精度较低(验证结果的决定系数小于0.45)。同时,研究评估了OLCI影像的3种大气校正方法(C2RCC、POLYMER、MUMM)在中国东部海域的适用性。结果显示,相对于其他两种大气校正算法,C2RCC在各波段有较好的表现(均方根误差小于0.004 8 sr−1)。将3种浮游植物类群反演模型应用到大气校正后的OLCI影像,验证结果显示,利用基于奇异值分解的多元线性回归法建立的硅藻叶绿素a浓度模型有较好的反演精度(决定系数为0.56)。
  • 图  1  研究区位置以及采样站位

    Fig.  1  Location of the study area and sampling stations

    图  2  基于奇异值分解的浮游植物类群Chl a浓度反演算法流程

    F表示本研究中使用的回归模型,即为多元线性回归模型或XGBoost回归模型

    Fig.  2  Flow chart of Chl a concentration inversion algorithm of phytoplankton groups based on singular value decomposition

    F represents the regression model used in this study, such as the multiple linear regression model or XGBoost regression model

    图  3  3种大气校正方法获得的遥感反射率与实测遥感反射率验证结果

    Fig.  3  Validation of remote sensing reflectance obtained by three atmospheric correction methods with in-situ remote sensing reflectance

    图  4  基于波段组合法建立的8类浮游植物叶绿素a浓度反演模型的验证结果(叶绿素a浓度单位:mg/m3

    Fig.  4  Validation results of Chl a concentration inversion model of eight phytoplankton groups based on band combination method (the unit of Chl a concentration is mg/m3)

    图  5  基于SVD+MLR建立的8类浮游植物叶绿素a浓度反演模型的验证结果(叶绿素a浓度单位:mg/m3

    Fig.  5  Validation results of Chl a concentration inversion model of eight phytoplankton groups based on SVD+MLR method (the unit of Chl a concentration is mg/m3)

    图  6  基于SVD+XGBoost建立的8类浮游植物叶绿素a浓度反演模型的验证结果(叶绿素a浓度单位:mg/m3

    Fig.  6  Validation results of Chl a concentration inversion model of eight phytoplankton groups based on SVD+XGBoost method (the unit of Chl a concentration is mg/m3)

    图  7  基于波段组合法的浮游植物类群叶绿素a浓度卫星反演验证(叶绿素a浓度单位:mg/m3

    Fig.  7  Validation of satellite inversion results of phytoplankton group Chl a concentration based on band combination method (the unit of Chl a concentration is mg/m3)

    图  9  基于SVD+XGBoost的浮游植物类群叶绿素a浓度卫星反演验证(叶绿素a浓度单位:mg/m3

    Fig.  9  Validation of satellite inversion results of phytoplankton group Chl a concentration based on SVD+XGBoost method (the unit of Chl a concentration is mg/m3)

    图  8  基于SVD+MLR的浮游植物类群叶绿素a浓度卫星反演验证 (叶绿素a浓度单位:mg/m3

    Fig.  8  Validation of satellite inversion results of phytoplankton group Chl a concentration based on SVD+MLR method (the unit of Chl a concentration is mg/m3)

    图  10  2020年5月和2020年8月中国东部海域硅藻叶绿素a浓度空间分布

    Fig.  10  The spatial distribution of diatom Chl a concentration in the eastern China seas in May 2020 and August 2020

    表  1  本研究采用的波段组合形式

    Tab.  1  The band combinations used in this study

    序号波段(B)组合形式序号波段(B)组合形式
    BC1$ {B}_{i}+{B}_{j} $BC6$\dfrac{ {B}_{i}-{B}_{j} }{ {B}_{k} }$
    BC2$ {B}_{i}-{B}_{j} $BC7$\dfrac{ {B}_{i} }{ {B}_{j}+{B}_{k} }$
    BC3$ {B}_{i}\times {B}_{j} $BC8$\dfrac{ {B}_{i} }{ {B}_{j}-{B}_{k} }$
    BC4$ {B}_{i}/{B}_{j} $BC9${B}_{i}\times \left(\dfrac{1}{ {B}_{j} }+\dfrac{1}{ {B}_{k} }\right)$
    BC5$\dfrac{ {B}_{i}+{B}_{j} }{ {B}_{k} }$BC10${B}_{i}\times \left(\dfrac{1}{ {B}_{j} }-\dfrac{1}{ {B}_{k} }\right)$
    下载: 导出CSV

    表  2  8类浮游植物叶绿素a浓度统计特征

    Tab.  2  Statistical characteristics of Chl a concentration of eight phytoplankton groups

    浮游植物站位数
    N
    平均值/
    (mg∙m−3
    标准差/
    (mg∙m−3
    最大值/
    (mg∙m−3
    青绿藻4500.150.274.45
    甲藻3800.290.8910.91
    隐藻4100.170.221.31
    绿藻2160.220.777.45
    蓝藻4650.240.689.85
    硅藻4461.132.1015.23
    金藻3600.110.232.51
    定鞭藻4580.080.141.74
    下载: 导出CSV

    表  3  3种大气校正算法的精度评价

    Tab.  3  Accuracy evaluation of three atmospheric correction algorithms

    波长/nmRMSE/(10−3 sr−1 MAE/(10−3 sr−1 MAPE/%
    C2RCCPOLYMERMUMMC2RCCPOLYMERMUMMC2RCCPOLYMERMUMM
    400 4.13 4.41 10.79 3.21 3.67 9.83 41 44 213
    412.5 3.99 4.36 7.68 3.01 3.53 6.69 36 41 141
    442.5 4.10 4.64 6.81 3.21 3.68 6.02 36 39 140
    490 4.79 4.97 4.90 3.74 3.82 4.43 40 37 89
    510 4.72 5.28 4.21 3.65 4.12 3.74 40 41 70
    560 4.34 5.31 3.53 3.31 4.03 3.04 33 43 56
    620 3.22 4.21 3.05 2.24 3.21 2.44 49 66 81
    665 3.21 3.75 2.71 2.21 2.82 2.05 61 72 84
    673.75 3.23 3.64 2.62 2.29 2.72 1.98 63 71 80
    681.25 3.21 3.64 2.60 2.29 2.75 1.91 64 73 83
    708.75 3.12 4.45 3.21 2.21 3.62 1.93 70 116 69
    753.75 3.21 3.24 2.38 2.30 2.40 1.49 88 94 81
    778.75 3.04 3.23 2.40 2.18 2.40 1.44 86 99 90
    下载: 导出CSV

    表  4  8类浮游植物叶绿素a浓度反演模型使用的波段组合形式及回归方程

    Tab.  4  Band combinations form and regression equations used in Chl a concentration inversion model of eight phytoplankton groups

    浮游植物波段组合与Chl a浓度对数的相关系数回归方程
    青绿藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(442.5\right)+{R}_{{\rm{rs}}}\left(620\right)}{ {R}_{{\rm{rs}}}\left(560\right)}}$0.53${ \mathrm{l}\mathrm{g}C_P=-0.74X+0.09 }$
    甲藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(442.5\right)}{ {R}_{{\rm{rs}}}\left(510\right)}-\dfrac{ {R}_{{\rm{rs}}}\left(442.5\right)}{ {R}_{{\rm{rs}}}\left(560\right)}}$0.54${ \mathrm{l}\mathrm{g}C_P=-7.83{X}^{3}-0.21{X}^{2}+3.97X-1.05 }$
    隐藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(442.5\right)+{R}_{{\rm{rs}}}\left(490\right)}{ {R}_{{\rm{rs}}}\left(510\right)}}$0.61${ \mathrm{l}\mathrm{g}C_P=-2.10X+2.87} $
    绿藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(560\right)}{ {R}_{{\rm{rs}}}\left(442.5\right)-{R}_{{\rm{rs}}}\left(620\right)}}$0.37${ \mathrm{l}\mathrm{g}C_P=2.2\times 1{0}^{-4}{X}^{2}-3.8\times {10}^{-2}{X}-1.25 }$
    蓝藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(412.5\right)}{ {R}_{{\rm{rs}}}\left(442.5\right)-{R}_{{\rm{rs}}}\left(620\right)}}$0.40${ \mathrm{l}\mathrm{g}C_P=-0.006X-0.951 }$
    硅藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(490\right)+{R}_{{\rm{rs}}}\left(620\right)}{ {R}_{{\rm{rs}}}\left(560\right)}}$0.76$ {\mathrm{l}\mathrm{g}C_P=-1.93X+2.75} $
    金藻$ {X={R}_{{\rm{rs}}}\left(665\right)-{R}_{{\rm{rs}}}\left(673.75\right) }$0.34${ \mathrm{l}\mathrm{g}C_P=-477\;853.92{X}^{2}-1\;569.03X-1.46 }$
    定鞭藻${X=\dfrac{ {R}_{{\rm{rs}}}\left(490\right)-{R}_{{\rm{rs}}}\left(510\right)}{ {R}_{{\rm{rs}}}\left(560\right)}}$0.42${ \mathrm{l}\mathrm{g}C_P=-2.143X-1.285} $
    下载: 导出CSV

    表  5  8类浮游植物叶绿素a浓度遥感反演模型的精度评价

    Tab.  5  Accuracy of inversion model for Chl a concentration of eight phytoplankton groups

    浮游植物类群建模方法R2MAERMSEMAPE/%
    青绿藻BC0.290.330.4429
    SVD+MLR0.380.330.4233
    SVD+XGBoost0.470.300.3826
    甲藻BC0.310.630.7640
    SVD+MLR0.420.620.6952
    SVD+XGBoost0.770.340.4424
    隐藻BC0.310.480.6041
    SVD+MLR0.430.420.5525
    SVD+XGBoost0.490.380.5229
    绿藻BC0.130.680.8046
    SVD+MLR−0.060.770.8956
    SVD+XGBoost0.320.560.7126
    蓝藻BC0.150.350.5224
    SVD+MLR0.060.390.5529
    SVD+XGBoost0.440.310.4321
    硅藻BC0.550.410.5771
    SVD+MLR0.640.390.5263
    SVD+XGBoost0.730.320.4548
    金藻BC−0.030.540.7127
    SVD+MLR0.060.520.6827
    SVD+XGBoost0.360.460.5625
    定鞭藻BC0.140.370.4624
    SVD+MLR0.370.320.4020
    SVD+XGBoost0.510.260.3513
    下载: 导出CSV
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  • 收稿日期:  2021-08-17
  • 修回日期:  2021-11-20
  • 刊出日期:  2022-04-15

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