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, China2Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, College of Oceanography, University of Chinese Academy of Sciences, Qingdao 266071, China3College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China4College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Chinashow less
DOI: 10.3969/j.issn.1673-6141.2023.06.007
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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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Fig. 1. Quasi-true color satellite images of Yellow River Estuary on October 24, 2017. (a) S2-MSI; (b) L7-ETM+; (c) S3-OLCI
Fig. 2. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI 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
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
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 water | Total Suspended Matter/(mg·L-1) | ρw, NIR |
|---|
| Highly turbid water | 10～100 | 0.008～0.060 | | Extremely turbid water | 100～1000+ | 0.060～0.200 |
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Table 1. Classification of turbidity degree of water
| Image date | Acquisition time | Spatial resolution /m | τ550 | Sensor |
|---|
| 2017-10-24 | 10:44 | 30 | 0.42 | ETM+ | | 2018-04-02 | 10:42 | 30 | 0.60 | ETM+ | | 2018-09-09 | 10:40 | 30 | < 0.1 | ETM+ | | 2019-02-16 | 10:35 | 30 | 0.09 | ETM+ | | 2019-01-23 | 10:41 | 30 | 0.13 | OLI | | 2019-03-12 | 10:41 | 30 | 0.10 | OLI | | 2017-10-24 | 10:47 | 10、20、60 | 0.42 | MSI | | 2018-04-02 | 10:45 | 10、20、60 | 0.60 | MSI | | 2018-09-09 | 10:45 | 10、20、60 | < 0.1 | MSI | | 2019-02-16 | 10:48 | 10、20、60 | 0.09 | MSI | | 2017-10-24 | 10:02 | 300 | 0.42 | OLCI | | 2018-09-09 | 10:05 | 300 | < 0.1 | OLCI | | 2019-01-23 | 10:41 | 300 | 0.13 | OLCI | | 2019-02-16 | 10:18 | 300 | 0.09 | OLCI | | 2019-03-12 | 10:35 | 300 | 0.10 | OLCI |
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Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ550 and sensor types of each image
| Atmospheric correction processor | Version/Software | Sentinel satellite and sensor |
|---|
| S2-MSI | S3-OLCI |
|---|
| ACOLITE | 20210802/ACOLITE | √ | √ | | iCOR | 3.0.0/SNAP 8.0 | √ | √ | | C2RCC | 2.1/SNAP 8.0 | √ | √ | | FLAASH | –/ENVI 5.6 | √ | √ | | Sen2Cor | 2.9.0/Sen2Cor | √ | |
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Table 3. List of atmospheric correction algorithms tested for S2-MSI and S3-OLCI
| Type of water | Sensor | Total number of pixels |
|---|
| Highly turbid water | MSI | 674303 | | OLCI | 6699 | | Extremely turbid water | MSI | 382878 | | OLCI | 1897 |
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Table 4. Total match-up pixel numbers of S2-MSI/S3-OLCI with Landsat sensors
| Algorithm | Band | Extremely turbid water | Highly turbid water |
|---|
| R2 | ERMS | EMARD/% | SBia/% | R2 | ERMS | EMARD/% | SBia/% |
|---|
| C2RCC | Green | 0.30 | 0.079 | 85.87 | -59.37 | 0.93 | 0.017 | 18.77 | -14.44 | | Red | 0.56 | 0.115 | 90.49 | -62.39 | 0.93 | 0.041 | 51.16 | -37.99 | | NIR | 0.78 | 0.102 | 158.82 | -88.88 | 0.05 | 0.020 | 102.81 | -67.18 | | Green+Red+NIR | 0.31 | 0.099 | 112.89 | -68.89 | 0.90 | 0.029 | 56.21 | -30.01 | | FLAASH | Green | 0.34 | 0.024 | 14.22 | 15.65 | 0.42 | 0.027 | 20.44 | 15.08 | | Red | 0.006 | 0.017 | 8.49 | 2.93 | 0.68 | 0.023 | 23.64 | 5.32 | | NIR | 0.65 | 0.020 | 15.28 | 14.63 | 0.25 | 0.023 | 50.36 | 65.93 | | Green+Red+NIR | 0.81 | 0.020 | 12.60 | 9.77 | 0.73 | 0.025 | 31.16 | 16.21 | | Sen2Cor | Green | 0.32 | 0.033 | 21.28 | 23.90 | 0.64 | 0.030 | 21.92 | 21.62 | | Red | 0.38 | 0.021 | 10.14 | 10.38 | 0.75 | 0.022 | 21.44 | 11.17 | | NIR | 0.80 | 0.032 | 24.46 | 27.04 | 0.29 | 0.026 | 55.45 | 77.21 | | Green+Red+NIR | 0.88 | 0.025 | 15.15 | 15.88 | 0.79 | 0.026 | 32.72 | 23.04 | | iCOR | Green | 0.0007 | 0.009 | 6.03 | -3.40 | 0.85 | 0.015 | 10.76 | -10.01 | | Red | 0.55 | 0.008 | 3.65 | 0.76 | 0.92 | 0.018 | 15.68 | -14.35 | | NIR | 0.84 | 0.020 | 15.53 | 16.73 | 0.52 | 0.009 | 35.11 | 1.88 | | Green+Red+NIR | 0.84 | 0.014 | 8.42 | 3.74 | 0.93 | 0.014 | 20.12 | -10.65 |
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Table 5. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI data in water with different turbidity
| Algorithm | Band | Extremely turbid water | Highly turbid water |
|---|
| R2 | ERMS | EMARD/% | SBia/% | R2 | ERMS | EMARD/% | SBia/% |
|---|
| C2RCC | Green | 0.03 | 0.026 | 17.93 | -12.06 | 0.33 | 0.015 | 8.85 | -4.44 | | Red | 0.09 | 0.059 | 34.69 | -29.18 | 0.77 | 0.024 | 20.65 | -17.23 | | NIR | 0.008 | 0.061 | 59.92 | -48.18 | 0.62 | 0.007 | 31.23 | -8.19 | | Green + Red + NIR | 0.42 | 0.052 | 38.07 | -29.51 | 0.91 | 0.017 | 20.17 | -10.62 | | FLAASH | Green | 0.13 | 0.026 | 16.96 | 18.42 | 0.62 | 0.017 | 11.94 | 12.69 | | Red | 0.32 | 0.011 | 4.79 | 3.39 | 0.84 | 0.011 | 8.33 | 5.15 | | NIR | 0.64 | 0.017 | 13.97 | -2.39 | 0.58 | 0.012 | 34.98 | 32.82 | | Green + Red + NIR | 0.81 | 0.019 | 11.89 | 6.20 | 0.96 | 0.014 | 18.38 | 11.06 | | iCOR | Green | 0.49 | 0.019 | 13.33 | 14.04 | 0.40 | 0.009 | 6.04 | 3.35 | | Red | 0.51 | 0.010 | 4.57 | 3.32 | 0.85 | 0.010 | 6.61 | -1.76 | | NIR | 0.74 | 0.015 | 12.62 | -4.18 | 0.49 | 0.011 | 46.45 | -0.24 | | Green + Red + NIR | 0.87 | 0.015 | 10.12 | 4.37 | 0.96 | 0.010 | 19.62 | 0.58 |
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Table 6. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI data in water with different turbidity
