Evaluation of applicability of Sentinel-2-MSI and Sentinel-3-OLCI water-leaving reflectance products in Yellow River Estuary

LUO Yafei; ZHONG Xiaojin; FU Dongyang; YAN Liwen; ZHANG Yi; LIU Yilin; HUANG Haijun; ZHANG Zehua; Qi Yali; WANG Qian

doi:10.3969/j.issn.1673-6141.2023.06.007

Journal of Atmospheric and Environmental Optics, Volume. 18, Issue 6, 585(2023)

Evaluation of applicability of Sentinel-2-MSI and Sentinel-3-OLCI water-leaving reflectance products in Yellow River Estuary

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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Figures & Tables(11)

Fig. 1. Quasi-true color satellite images of Yellow River Estuary on October 24, 2017. (a) S2-MSI; (b) L7-ETM+; (c) S3-OLCI

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Fig. 2. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI in YRE. (a)―(d) Extremely turbid water; (e)―(h) highly turbid water

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Fig. 3. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI in YRE. (a)―(d) Extremely turbid water; (e)―(h) highly turbid water

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Fig. 4. The spatial distribution of water-leaving reflectance ρ_w 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

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Fig. 5. Scatterplots of the comparison of ρ_w between different sensors corrected by ACOLITE DSF algorithm. (a), (b) Green band; (c), (d) red band; (e), (f) near-infrared band

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Table 1. Classification of turbidity degree of water
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Table 1. Classification of turbidity degree of water
Type of water Total Suspended Matter/(mg·L^-1) ρ_w_{, NIR}
Highly turbid water 10～100 0.008～0.060
Extremely turbid water 100～1000+ 0.060～0.200

Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ₅₅₀ and sensor types of each image

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Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ₅₅₀ and sensor types of each image

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

Table 3. List of atmospheric correction algorithms tested for S2-MSI and S3-OLCI

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Table 3. List of atmospheric correction algorithms tested for S2-MSI and S3-OLCI

Atmospheric correction processor	Version/Software	Sentinel satellite and sensor
Atmospheric correction processor	Version/Software	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	√

Table 4. Total match-up pixel numbers of S2-MSI/S3-OLCI with Landsat sensors
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Table 4. Total match-up pixel numbers of S2-MSI/S3-OLCI with Landsat sensors
Type of water Sensor Total number of pixels
Highly turbid water MSI 674303
OLCI 6699
Extremely turbid water MSI 382878
OLCI 1897

Table 5. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI data in water with different turbidity

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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
Algorithm	Band	R²	E_RMS	E_MARD/%	S_Bia/%	R²	E_RMS	E_MARD/%	S_Bia/%
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

Table 6. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI data in water with different turbidity

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Table 6. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI data in water with different turbidity

Algorithm	Band	Extremely turbid water				Highly turbid water
Algorithm	Band	R²	E_RMS	E_MARD/%	S_Bia/%	R²	E_RMS	E_MARD/%	S_Bia/%
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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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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Paper Information

Category:

Received: May. 17, 2022

Accepted: --

Published Online: Dec. 22, 2023

The Author Email: YAN Liwen (yanliwen@qdio.ac.cn), ZHANG Yi (yizhang@sdust.edu.cn)

DOI:10.3969/j.issn.1673-6141.2023.06.007

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Classification of turbidity degree of water

Table 1. Classification of turbidity degree of water

Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ550 and sensor types of each image

Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ550 and sensor types of each image

Table 3. List of atmospheric correction algorithms tested for S2-MSI and S3-OLCI

Table 3. List of atmospheric correction algorithms tested for S2-MSI and S3-OLCI

Table 4. Total match-up pixel numbers of S2-MSI/S3-OLCI with Landsat sensors

Table 4. Total match-up pixel numbers of S2-MSI/S3-OLCI with Landsat sensors

Table 5. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI data in water with different turbidity

Table 5. Accuracy evaluation of different atmospheric correction algorithms for S2-MSI data in water with different turbidity

Table 6. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI data in water with different turbidity

Table 6. Accuracy evaluation of different atmospheric correction algorithms for S3-OLCI data in water with different turbidity

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Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ₅₅₀ and sensor types of each image

Table 2. The acquisition time，spatial resolution，aerosol optical thickness τ₅₅₀ and sensor types of each image