Laser & Optoelectronics Progress, Volume. 56, Issue 11, 110101(2019)
Remote Sensing to Estimate Sea-Surface Density of Yellow and Bohai Seas off the East Coast of China
Figures & Tables(8)
Fig. 2. Correlation between single band, band ratio remote sensing reflectivity and SSD. (a) Rrs in single band; (b) Rrs in band ratio
Fig. 3. Retrieval accuracy comparison of models. (a) Model of 490 nm single band; (b) model of 555 nm single band; (c) model of 660 nm single band; (d) multiple linear regression model. Solid line is 1∶1 and dotted line is ±10% error; (e) SMAPE and SRMSE of single band models
Fig. 5. Statistical relationship analysis between SSD and total backscattering coefficient. Correlation analysis between SSD and measured backscattering coefficient in (a) 442 nm band, (b) 448 nm band, (c) 550 nm band
Table 1. Spectral bands of GOCI and other related description information
View tableTable 1. Spectral bands of GOCI and other related description information
Band /nm Band center / nm Band width /nm Nominal radiance Saturation radiance Signal noise ratio Primary use B1 412 20 100 152 1000 Yellow substance and turbidity B2 443 20 92.5 148 1090 Chlorophyll absorption maximum B3 490 20 72.2 116 1170 Chlorophyll and other pigments B4 555 20 55.3 87 1070 Turbidity, suspended sediment B5 660 20 32 61 1010 Baseline of fluorescence signal, Chlorophyll, suspended sediment B6 680 10 27.1 47 870 Atmospheric correction and fluorescence signal B7 745 20 17.7 33 860 Atmospheric correction and baseline of fluorescence signal B8 865 40 12 24 750 Aerosol optical thickness, vegetation, and water vapor reference over the ocean
Table 2. Correlation between GOCI data of Rrs(λ1)/Rrs(λ2) and SSD in Yellow and Bohai Seas off the east coast of China
View tableTable 2. Correlation between GOCI data of Rrs(λ1)/Rrs(λ2) and SSD in Yellow and Bohai Seas off the east coast of China
λ 2 /nmCorrelation λ 1=412 nm λ 1=443 nm λ 1=490 nm λ 1=555 nm λ 1=660 nm λ 1=680 nm 412 - 0.32 0.37 0.29 0 0.17 443 0.37 - 0.47 0.26 0.10 0.26 490 0.48 0.50 - 0 0.10 0.41 555 0.22 0.10 0.10 - 0 0.46 660 0.24 0.30 0.36 0.41 - 0.37 680 0 0.14 0.20 0.26 0.10 -
Table 3. Precision analysis of retrieval models of SSD and remote sensing reflectance
View tableTable 3. Precision analysis of retrieval models of SSD and remote sensing reflectance
Band combination Model type Model equation R 2S MAPES RMSEExponential y =29.932exp(0.1343x )0.62 3.44 1.30 lg[ R rs(490)]Linear polynomial y =3.0688x +29.0950.63 3.44 1.30 Quadratic polynomial y =0.4456x 2 +4.8611x +30.790.64 3.30 1.30 Exponential y =29.568exp(0.1252x )0.55 4.02 1.60 lg[ R rs(555)]Linear polynomial y = 2.8678x +28.8380.57 4.07 1.63 Quadratic polynomial y =1.5787x 2+9.4064x +35.1850.60 3.72 1.51 Exponential y =28.159exp(0.0817x )0.56 3.95 1.58 lg[ R rs(660)]Linear polynomial y =1.8706x +27.7190.58 3.99 2.97 Quadratic polynomial y =0.5237x 2+4.5843x +30.9020.59 3.51 1.52 lg[ R rs(λi )]λi =490, 555, 660 nmMultiple single band linear equations Eq. (5) 0.70 3.49 1.04
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Xiaoping Su, Deyong Sun, Shengqiang Wang, Zhongfeng Qiu, Yu Huan. Remote Sensing to Estimate Sea-Surface Density of Yellow and Bohai Seas off the East Coast of China[J]. Laser & Optoelectronics Progress, 2019, 56(11): 110101
Paper Information
Category: Atmospheric Optics and Oceanic Optics
Received: Oct. 26, 2018
Accepted: Jan. 2, 2019
Published Online: Jun. 13, 2019
The Author Email: Deyong Sun (sundeyong1984@163.com)
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