Inversion Algorithm for Turbidity of Bohai and Yellow Seas Based on NPP-VIIRS Satellite Data

Mengjiao Ding; Zhongfeng Qiu; Hailong Zhang; Zhaoxin Li; Ying Mao

doi:10.3788/AOS201939.0601002

Acta Optica Sinica, Volume. 39, Issue 6, 0601002(2019)

Inversion Algorithm for Turbidity of Bohai and Yellow Seas Based on NPP-VIIRS Satellite Data

Mengjiao Ding, Zhongfeng Qiu^*, Hailong Zhang, Zhaoxin Li, and Ying Mao

Author Affiliations

School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China

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Figures & Tables(10)

Fig. 1. Study area, location of buoy, and sampling stations of different cruises

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Fig. 2. Turbidity statistics of water bodies based on (a) buoy and (b) cruise observation

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Fig. 3. Correlation coefficient between Rrs under different forms and T versus wavelength

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Fig. 4. Turbidity and result of inversion model developed using X1 (blue dots are matching dataset of measured data and satellite data). (a) Model calibration; (b) model validation

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Fig. 5. Turbidity and result of inversion model developed using X2 (blue dots are matching dataset of measured data and satellite data). (a) Model calibration; (b) model validation

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Fig. 6. Scatter plots of measured and estimated turbidity obtained by different models after adding ±5% random errors to Rrs at 443 nm and 486 nm (blue dots are matching dataset of measured data and satellite data). (a) X1 model; (b) X2 model

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Fig. 7. Monthly average distributions of water turbidity in Bohai and Yellow seas (2012—2018)

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Fig. 8. Seasonal average distributions of water turbidity in Bohai and Yellow seas (2012—2018)

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Table 1. Comparison of correlation coefficients between lg T and lg Rrs under different band combinations

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Table 1. Comparison of correlation coefficients between lg T and lg Rrs under different band combinations

X	General form	Best band combination	R²
X1	lg R_rs(λ₃)	λ₃=486 nm	0.988
X2	$\frac{\lg R_{rs} (λ_{2}) + \lg R_{rs} (λ_{3})}{\lg R_{rs} (λ_{2}) / \lg R_{rs} (λ_{3})}$	λ₂=443 nm,λ₃=486 nm	0.988
X3	lg R_rs(λ₄)-lg R_rs(λ₅)	λ₄=551 nm,λ₅=671 nm	0.902
X4	lg R_rs(λ₄)/lg R_rs(λ₅)	λ₄=551 nm,λ₅=671 nm	0.729
X5	$\frac{l g R_{rs} (λ_{3}) - \lg R_{rs} (λ_{5})}{\lg R_{rs} (λ_{3}) + \lg R_{rs} (λ_{5})}$	λ₃=486 nm,λ₅=671 nm	0.729
X6	$\frac{\lg R_{rs} (λ_{4}) - \lg R_{rs} (λ_{5})}{\lg R_{rs} (λ_{4}) / \lg R_{rs} (λ_{5})}$	λ₄=551 nm,λ₅=671 nm	0.934

Table 2. Statistics of coefficients of turbidity inversion model and its accuracy evaluation parameters
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Table 2. Statistics of coefficients of turbidity inversion model and its accuracy evaluation parameters
Independent variable Coefficient of model N R² S_RMSE /NTU S_MAE /NTU S_MRE /%
a b
X1X2 3.4361.684 8.0247.784 3232 0.9740.971 16.09.4 2317 34.6337.93

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Mengjiao Ding, Zhongfeng Qiu, Hailong Zhang, Zhaoxin Li, Ying Mao. Inversion Algorithm for Turbidity of Bohai and Yellow Seas Based on NPP-VIIRS Satellite Data[J]. Acta Optica Sinica, 2019, 39(6): 0601002

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Paper Information

Category: Atmospheric Optics and Oceanic Optics

Received: Dec. 18, 2018

Accepted: Mar. 4, 2019

Published Online: Jun. 17, 2019

The Author Email: Qiu Zhongfeng (zhongfeng.qiu@nuist.edu.cn)

DOI:10.3788/AOS201939.0601002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison of correlation coefficients between lg T and lg Rrs under different band combinations

Table 1. Comparison of correlation coefficients between lg T and lg Rrs under different band combinations

Table 2. Statistics of coefficients of turbidity inversion model and its accuracy evaluation parameters

Table 2. Statistics of coefficients of turbidity inversion model and its accuracy evaluation parameters