• Journal of Atmospheric and Environmental Optics
  • Vol. 18, Issue 6, 585 (2023)
LUO Yafei1,2, ZHONG Xiaojin1, FU Dongyang1, YAN Liwen2,*..., ZHANG Yi3,**, LIU Yilin4, HUANG Haijun2, ZHANG Zehua2, Qi Yali1 and WANG Qian4|Show fewer author(s)
Author Affiliations
  • 1Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
  • 2Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, College of Oceanography, University of Chinese Academy of Sciences, Qingdao 266071, China
  • 3College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
  • 4College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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    DOI: 10.3969/j.issn.1673-6141.2023.06.007 Cite this Article
    Yafei LUO, Xiaojin ZHONG, Dongyang FU, Liwen YAN, Yi ZHANG, Yilin LIU, Haijun HUANG, Zehua ZHANG, Yali Qi, Qian WANG. Evaluation of applicability of Sentinel-2-MSI and Sentinel-3-OLCI water-leaving reflectance products in Yellow River Estuary[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(6): 585 Copy Citation Text show less
    Quasi-true color satellite images of Yellow River Estuary on October 24, 2017. (a) S2-MSI; (b) L7-ETM+; (c) S3-OLCI
    Fig. 1. Quasi-true color satellite images of Yellow River Estuary on October 24, 2017. (a) S2-MSI; (b) L7-ETM+; (c) S3-OLCI
    Accuracy evaluation of different atmospheric correction algorithms for S2-MSI in YRE. (a)―(d) Extremely turbid water; (e)―(h) highly turbid water
    Fig. 2. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI in YRE. (a)―(d) Extremely turbid water; (e)―(h) highly turbid water
    Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI in YRE. (a)―(d) Extremely turbid water; (e)―(h) highly turbid water
    Fig. 3. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI in YRE. (a)―(d) Extremely turbid water; (e)―(h) highly turbid water
    The spatial distribution of water-leaving reflectance ρw derived by ACOLITE DSF algorithm for S2-MSI, L7-ETM+ and S3-OLCI in green, red and near-infrared bands. (a)―(c) S2-MSI; (d)―(f) L7-ETM+; (g)―(i) S3-OLCI
    Fig. 4. The spatial distribution of water-leaving reflectance ρw derived by ACOLITE DSF algorithm for S2-MSI, L7-ETM+ and S3-OLCI in green, red and near-infrared bands. (a)―(c) S2-MSI; (d)―(f) L7-ETM+; (g)―(i) S3-OLCI
    Scatterplots of the comparison of ρw between different sensors corrected by ACOLITE DSF algorithm. (a), (b) Green band; (c), (d) red band; (e), (f) near-infrared band
    Fig. 5. Scatterplots of the comparison of ρw between different sensors corrected by ACOLITE DSF algorithm. (a), (b) Green band; (c), (d) red band; (e), (f) near-infrared band
    Type of waterTotal Suspended Matter/(mg·L-1)ρw, NIR
    Highly turbid water10~1000.008~0.060
    Extremely turbid water100~1000+0.060~0.200
    Table 1. Classification of turbidity degree of water
    Image dateAcquisition timeSpatial resolution /mτ550Sensor
    2017-10-2410:44300.42ETM+
    2018-04-0210:42300.60ETM+
    2018-09-0910:4030< 0.1ETM+
    2019-02-1610:35300.09ETM+
    2019-01-2310:41300.13OLI
    2019-03-1210:41300.10OLI
    2017-10-2410:4710、20、600.42MSI
    2018-04-0210:4510、20、600.60MSI
    2018-09-0910:4510、20、60< 0.1MSI
    2019-02-1610:4810、20、600.09MSI
    2017-10-2410:023000.42OLCI
    2018-09-0910:05300< 0.1OLCI
    2019-01-2310:413000.13OLCI
    2019-02-1610:183000.09OLCI
    2019-03-1210:353000.10OLCI
    Table 2. The acquisition time,spatial resolution,aerosol optical thickness τ550 and sensor types of each image
    Atmospheric correction processorVersion/SoftwareSentinel satellite and sensor
    S2-MSIS3-OLCI
    ACOLITE20210802/ACOLITE
    iCOR3.0.0/SNAP 8.0
    C2RCC2.1/SNAP 8.0
    FLAASH–/ENVI 5.6
    Sen2Cor2.9.0/Sen2Cor
    Table 3. List of atmospheric correction algorithms tested for S2-MSI and S3-OLCI
    Type of waterSensorTotal number of pixels
    Highly turbid waterMSI674303
    OLCI6699
    Extremely turbid waterMSI382878
    OLCI1897
    Table 4. Total match-up pixel numbers of S2-MSI/S3-OLCI with Landsat sensors
    AlgorithmBandExtremely turbid waterHighly turbid water
    R2ERMSEMARD/%SBia/%R2ERMSEMARD/%SBia/%
    C2RCCGreen0.300.07985.87-59.370.930.01718.77-14.44
    Red0.560.11590.49-62.390.930.04151.16-37.99
    NIR0.780.102158.82-88.880.050.020102.81-67.18
    Green+Red+NIR0.310.099112.89-68.890.900.02956.21-30.01
    FLAASHGreen0.340.02414.2215.650.420.02720.4415.08
    Red0.0060.0178.492.930.680.02323.645.32
    NIR0.650.02015.2814.630.250.02350.3665.93
    Green+Red+NIR0.810.02012.609.770.730.02531.1616.21
    Sen2CorGreen0.320.03321.2823.900.640.03021.9221.62
    Red0.380.02110.1410.380.750.02221.4411.17
    NIR0.800.03224.4627.040.290.02655.4577.21
    Green+Red+NIR0.880.02515.1515.880.790.02632.7223.04
    iCORGreen0.00070.0096.03-3.400.850.01510.76-10.01
    Red0.550.0083.650.760.920.01815.68-14.35
    NIR0.840.02015.5316.730.520.00935.111.88
    Green+Red+NIR0.840.0148.423.740.930.01420.12-10.65
    Table 5. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI data in water with different turbidity
    AlgorithmBandExtremely turbid waterHighly turbid water
    R2ERMSEMARD/%SBia/%R2ERMSEMARD/%SBia/%
    C2RCCGreen0.030.02617.93-12.060.330.0158.85-4.44
    Red0.090.05934.69-29.180.770.02420.65-17.23
    NIR0.0080.06159.92-48.180.620.00731.23-8.19
    Green + Red + NIR0.420.05238.07-29.510.910.01720.17-10.62
    FLAASHGreen0.130.02616.9618.420.620.01711.9412.69
    Red0.320.0114.793.390.840.0118.335.15
    NIR0.640.01713.97-2.390.580.01234.9832.82
    Green + Red + NIR0.810.01911.896.200.960.01418.3811.06
    iCORGreen0.490.01913.3314.040.400.0096.043.35
    Red0.510.0104.573.320.850.0106.61-1.76
    NIR0.740.01512.62-4.180.490.01146.45-0.24
    Green + Red + NIR0.870.01510.124.370.960.01019.620.58
    Table 6. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI data in water with different turbidity
    Yafei LUO, Xiaojin ZHONG, Dongyang FU, Liwen YAN, Yi ZHANG, Yilin LIU, Haijun HUANG, Zehua ZHANG, Yali Qi, Qian WANG. Evaluation of applicability of Sentinel-2-MSI and Sentinel-3-OLCI water-leaving reflectance products in Yellow River Estuary[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(6): 585
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