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中高空间分辨率宽波段光学卫星传感器参数赤潮探测影响研究

葛化鑫 刘荣杰 赵鑫 马毅 王新念 王义衎

葛化鑫,刘荣杰,赵鑫,等. 中高空间分辨率宽波段光学卫星传感器参数赤潮探测影响研究[J]. 海洋学报,2022,44(12):136–147 doi: 10.12284/hyxb2022161
引用本文: 葛化鑫,刘荣杰,赵鑫,等. 中高空间分辨率宽波段光学卫星传感器参数赤潮探测影响研究[J]. 海洋学报,2022,44(12):136–147 doi: 10.12284/hyxb2022161
Ge Huaxin,Liu Rongjie,Zhao Xin, et al. Impact of medium and high spatial resolution wide band optical satellite sensor parameters on red tide detection[J]. Haiyang Xuebao,2022, 44(12):136–147 doi: 10.12284/hyxb2022161
Citation: Ge Huaxin,Liu Rongjie,Zhao Xin, et al. Impact of medium and high spatial resolution wide band optical satellite sensor parameters on red tide detection[J]. Haiyang Xuebao,2022, 44(12):136–147 doi: 10.12284/hyxb2022161

中高空间分辨率宽波段光学卫星传感器参数赤潮探测影响研究

doi: 10.12284/hyxb2022161
基金项目: 国家自然科学基金重大项目(61890964);中韩海洋科学共同研究中心项目(PI-2022-1)。
详细信息
    作者简介:

    葛化鑫(1998-),男,河南省商丘市人,主要从事赤潮遥感探测研究。E-mail:ghx98826@163.com

    通讯作者:

    刘荣杰,副研究员,主要从事海洋光学遥感研究。E-mail: liurj@fio.org.cn

  • 中图分类号: TP79

Impact of medium and high spatial resolution wide band optical satellite sensor parameters on red tide detection

  • 摘要: 中高空间分辨率宽波段光学卫星已成为赤潮监测的主要数据源,但与水色卫星传感器不同,中高空间分辨率卫星传感器主要面向陆地应用,其波段数量少、宽度大,由此对赤潮探测带来的影响尚待研究。为此,本文基于不同优势种赤潮实测高光谱数据、时空同步的GF-1 WFV2、GF-1 WFV3传感器影像、Sentinel-2A MSI传感器影像及GF-6 WFV传感器影像,探究了波段设置、光谱响应函数、信噪比及空间分辨率对赤潮探测的影响,并分析了红边波段赤潮探测优势。结果表明:波段设置对赤潮探测影响大,特别是红光波段和红边波段的中心波长和波段宽度;波段设置相同的情况下,赤潮探测精度受光谱响应函数的影响大,受信噪比的影响较小;空间分辨率对赤潮探测的影响较大,空间分辨率的提升有助于提高赤潮探测的精度。红边波段赤潮探测实验表明,较之红光波段,基于红边波段的赤潮探测具有明显的优势,平均F1-Score提高了11%。本文的研究结果一方面可为赤潮中高空间分辨率卫星探测的数据选取提供理论依据,另一方面可为中高空间分辨率卫星传感器的设计提供参考。
  • 图  1  研究区及赤潮卫星影像示意图

    Fig.  1  Schematic diagram of the study area and satellite images of red tide

    图  2  不同优势种赤潮及不同类型海水实测高光谱数据

    Fig.  2  Hyperspectral data of different dominant species of red tide and different types of seawater

    图  3  信噪比评估流程图

    Fig.  3  Flow chart of signal to noise ratio evaluation

    图  4  6景宽幅相机影像蓝光波段不同阈值对应不同信噪比

    Fig.  4  Different signal-to-noise ratios were obtained by calculating different thresholds at the blue band of six wide field of view images

    图  5  蓝光波段不同阈值对应噪声分布

    Fig.  5  Noise distribution of different thresholds in blue band

    图  6  宽幅相机传感器绿光、红光、近红外波段噪声分布

    Fig.  6  Distribution of noise in the green, red and near-infrared bands of the wide field of view sensor

    图  7  不同卫星波段设置示意图

    Fig.  7  Diagram of different satellite band settings

    图  8  不同卫星光谱响应函数

    Fig.  8  Spectral response functions of different satellites

    图  9  不同优势种赤潮及不同类型海水中高空间分辨率宽波段卫星模拟遥感反射率

    Fig.  9  Medium and high resolution broad-band satellite remote sensing reflectance simulated from different dominant species of red tide and different types of seawater

    图  10  GF-1 WFV传感器赤潮检测结果

    Fig.  10  Results of GF-1 WFV sensor red tide detection

    图  11  GF-1 WFV2、GF-1 WFV3光谱响应函数

    Fig.  11  Spectral response functions of GF-1 WFV2 and GF-1 WFV3

    图  12  GF-1 WFV3检测出赤潮GF-1 WFV2未检测出赤潮像元辐亮度

    Fig.  12  Irradiance of image elements where red tide was detected by GF-1 WFV3 but not by GF-1 WFV2

    图  13  GF-1 WFV3与GF-1 WFV2 GF1_RI指数散点图

    Fig.  13  Scatter plot of GF1_RI index calculated by GF-1 WFV3 and GF-1 WFV2

    图  14  不同空间分辨率Sentinel-2A MSI影像赤潮检测结果

    Fig.  14  Results of red tide detection with Sentinel-2A MSI images at different spatial resolutions

    图  15  赤潮Sentinel-2A MSI影像探测结果

    Fig.  15  Red tide detection results from Sentinel-2A MSI images

    图  16  赤潮GF-6 WFV影像探测结果

    Fig.  16  Red tide detection results from GF-6 WFV images

    表  1  中高空间分辨率卫星传感器参数

    Tab.  1  Medium and high spatial resolution satellite sensor parameters

    卫星传感器波段光谱范围/nm中心波长/nm空间分辨率/m幅宽/km重访周期/d卫星发射时间
    GF-1 WFV1450~5204851680022013年
    2520~6005551680022013年
    3630~6906601680022013年
    4770~8908301680022013年
    HY-1C CZI1420~5004605095032018年
    2520~6005605095032018年
    3610~6906505095032018年
    4760~8908255095032018年
    GF-6 WFV1450~5204851680022018年
    2520~5905551680022018年
    3630~6906601680022018年
    4770~8908301680022018年
    5690~7307101680022018年
    6730~7707501680022018年
    7400~4504251680022018年
    8590~6306101680022018年
    Landsat8 OLI1433~45344330170162013年
    2450~51548330170162013年
    3525~60056330170162013年
    4630~68065530170162013年
    5845~88586530170162013年
    Sentinel-2A MSI1433~45344360290102015年
    2458~52349010290102015年
    3543~57856010290102015年
    4650~68066510290102015年
    5698~71370520290102015年
    6733~74874020290102015年
    7773~79378320290102015年
    8785~90084210290102015年
    下载: 导出CSV

    表  2  GF-1 WFV2、GF-1 WFV3传感器信噪比评估结果

    Tab.  2  Signal-to-noise ratio evaluation results of GF-1 WFV2 and GF-1 WFV3 sensors

    波长/nmWFV2
    SNR
    WFV3
    SNR
    差异Ltypical/
    (mW·cm−2·μm−1·sr−1
    Ltypical
    标准差
    48526325855.231.01
    55510784232.850.61
    6607964151.270.30
    8306144170.430.11
    下载: 导出CSV

    表  3  GF-1 WFV2、GF-1 WFV3传感器赤潮探测精度

    Tab.  3  Red tide detection accuracy of GF-1 WFV2 and GF-1 WFV3 sensors

    影像OA/%Recall/%Precision/%F1-ScoreKappa系数
    WFV294.4773.1598.510.840.807
    WFV397.2792.7393.430.930.914
    下载: 导出CSV

    表  4  不同空间分辨率赤潮探测精度

    Tab.  4  Accuracy of red tide detection at different spatial resolutions

    分辨率/mOA/%Recall/%F1-ScoreKappa系数
    1099.6262.750.7650.763
    2099.5560.000.7480.746
    6098.8043.640.6080.603
    10098.3336.700.5370.530
    20097.1815.380.2670.260
    下载: 导出CSV

    表  5  Sentinel-2A MSI不同波段GF1_RI指数赤潮探测精度

    Tab.  5  Accuracy of GF1_RI index red tide detection at different wavelengths calculated by Sentinel-2A MSI

    波段Recall/%F1-ScoreKappa系数
    红光波段44.10.590.58
    红边波段60.80.700.69
    下载: 导出CSV

    表  6  GF-6 WFV不同波段GF1_RI指数赤潮探测精度

    Tab.  6  Accuracy of GF1_RI index red tide detection at different wavelengths calculated by GF-6 WFV

    波段Recall/%F1-ScoreKappa系数
    红光波段79.80.870.86
    红边波段82.60.900.90
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-07
  • 修回日期:  2022-07-15
  • 网络出版日期:  2022-10-08
  • 刊出日期:  2023-01-17

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