Spacecraft Recovery & Remote Sensing, Volume. 45, Issue 5, 123(2024)

Research on Area Correction for Monitoring Cyanobacterial Bloom with Medium and Low Resolution Satellites: Taking GOCI-2 as an Example

Yaping WANG1, Xifei XU1,2, Jiaguo LI2、*, Xingfeng CHEN2, Ning ZHANG3, Huajie CHEN4, Limin ZHAO2, and Jun LIU2
Author Affiliations
  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
  • 2Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100094, China
  • 3China Academy of Urban Planning and Design, Beijing 100044, China
  • 4Satellite Application Center for Ecology and Environment, Beijing 100094, China
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    Figures & Tables(12)
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    • Table 1. The time and wind speed of Sentinel-2 and GOCI-2 image

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      Table 1. The time and wind speed of Sentinel-2 and GOCI-2 image

      日期Sentinel-2过境时刻GOCI-2过境时刻时间差/min风速/(m/s)用途
      2021-12-0410:4110:15261.80建模样本数据源
      2022-03-0410:3610:15210.56建模样本数据源
      2022-05-0310:3510:15202.90建模样本数据源
      2022-07-0710:3510:15203.64验证数据
      2022-09-1010:3510:15202.53验证数据
      2022-10-1510:3610:15212.91建模样本数据源
      2022-12-1910:4110:15261.97验证数据
      2023-08-1110:3510:15200.42验证数据
    • Table 2. Precision of fitting NDVI to cyanobacterial bloom area ratio

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      Table 2. Precision of fitting NDVI to cyanobacterial bloom area ratio

      回归模型表达式模型参数决定系数均方根误差
      注:x为GOCI-2像元NDVI值。
      一次多项式$ r = {k_0} + {k_1}x $$ {k}_{0} $=0.830 6;$ {k}_{1} $=0.774 30.668 740.115
      二次多项式$ r = {k_0} + {k_1}x + {k_2}x_{}^2 $$ {k}_{0} $=0.923 6;$ {k}_{1} $=0.847 6;$ {k}_{2} $=−2.0840.854 200.077
      三次多项式$ r = {k_0} + {k_1}x + {k_2}x_{}^2 + {k_3}x_{}^3 $$ {k}_{0} $=0.931 8;$ {k}_{1} $=0.481 3;$ {k}_{2} $=−2.386 6;$ {k}_{3} $=4.479 40.886 790.068
      四次多项式$ r = {k_0} + {k_1}x + {k_2}x_{}^2 + {k_3}x_{}^3 + {k_4}x_{}^4 $$ {k}_{0} $=0.86;$ {k}_{1} $=0.96;$ {k}_{2} $=−2.85;$ {k}_{3} $=4.07;$ {k}_{4} $=−2.180.895 450.065
      Logistic模型$ r = \dfrac{{{k_1}}}{{{\text{1}} + {\text{e}}_{}^{ - {k_2}{\text{(}}x - {k_3}{\text{)}}}}} $$ {k}_{1} $=0.974 5;$ {k}_{2} $= 11.36;$ {k}_{3} $=−0.285 70.881 010.069
    • Table 3. Comparison and validation of GOCI-2 cyanobacterial bloom area extraction results based on Sentinel-2

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      Table 3. Comparison and validation of GOCI-2 cyanobacterial bloom area extraction results based on Sentinel-2

      日期Sentinel-2水华面积/m2GOCI-2水华面积/m2GOCI-2提取精度/%
      校正前校正后校正前校正后
      2022-07-07557.37688.87621.7076.488.5
      2022-09-10463.61595.33480.7371.696.3
      2022-12-19149.21210.77168.0258.787.4
      2023-08-11271.61367.78303.8864.688.1
    • Table 4. NDVI band combination methods

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      Table 4. NDVI band combination methods

      波段组合方式红光波段近红外波段
      注:本文2.2节采用的NDVI波段组合方式为NDVI_1。
      NDVI_1B8(660 nm)B12(865 nm)
      NDVI_2B8(660 nm)B11(745 nm)
      NDVI_3B7(620 nm)B11(745 nm)
      NDVI_4B7(620 nm)B12(865 nm)
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    Yaping WANG, Xifei XU, Jiaguo LI, Xingfeng CHEN, Ning ZHANG, Huajie CHEN, Limin ZHAO, Jun LIU. Research on Area Correction for Monitoring Cyanobacterial Bloom with Medium and Low Resolution Satellites: Taking GOCI-2 as an Example[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(5): 123

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    Paper Information

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    Received: Jan. 11, 2024

    Accepted: --

    Published Online: Nov. 13, 2024

    The Author Email: LI Jiaguo (lijg@aircas.ac.cn)

    DOI:10.3969/j.issn.1009-8518.2024.05.012

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