Remote Sensing Estimation of Suspended Solids Concentrations in Coastal and Inland Waters Based on Quasi-Analytical Algorithm

Quansheng Lou; Chunchao Zhang; Wei Deng; Wen Su; Guangjia Jiang; Ronghua Ma

doi:10.3788/AOS202141.1901003

Acta Optica Sinica, Volume. 41, Issue 19, 1901003(2021)

Remote Sensing Estimation of Suspended Solids Concentrations in Coastal and Inland Waters Based on Quasi-Analytical Algorithm

Quansheng Lou^1,2, Chunchao Zhang¹, Wei Deng^1,2, Wen Su³, Guangjia Jiang^1,2、*, and Ronghua Ma^4、**

Author Affiliations

¹South China Sea Environment Monitoring Center, State Oceanic Administration, Guangzhou, Guangdong 510300, China

²Nansha Islands Coral Reef Ecosystem Observation and Research Station, Ministry of Natural Resources, Guangzhou, Guangdong 510300, China

³South China Sea Institute of Planning and Environmental Research, State Oceanic Administration, Guangzhou, Guangdong 510300, China

⁴State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, China

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Figures & Tables(11)

Fig. 1. Distribution of sampling sites. (a) Taih Lake; (b) Chaohu Lake; (c) Poyang Lake; (d) Pearl River Estuary；(e) Daya Bay; (f) Shitoukoumen Reservoir

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Fig. 2. Spatial distribution of quasi－synchronous stations from satellite observations and field measurements

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Fig. 3. R_rs of measured and satellite remote sensing images. (a) R_rs(555) and R_rs(865); (b) remote sensing reflectance spectra

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Fig. 4. Estimation results of TSS concentration in coastal and inland waters

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Fig. 5. Relationship between measured c_TSS and estimated c_TSS in Shitoukoumen Reservoir

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Fig. 6. Temporal and spatial changes of c_TSS in different water bodies. (a) Taihu Lake; (b) Pearl River Estuary

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Fig. 7. Variation curve of c_TSS in different water bodies with latitude profile distribution. (a) Taihu Lake; (b) Pearl River Estuary

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Table 1. Sampling sites information in the study area

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Table 1. Sampling sites information in the study area

Study area	Sampling date	Proxy	Sampling site
Taihu Lake	Apr. 23－May 2, 2010	THL－201004	102
Chaohu Lake	Oct. 15－16, 2009	CHL－200910	37
Poyang Lake	Oct. 15－17, 2010	PYL－201010	47
Pearl River Estuary	Aug. 11－15, 2014	PRE－201408	21
Daya Bay	Aug. 19－21, 2014	DYB－201408	24
Shitoukoumen Reservoir	May 20, 2009	STKM－200905	17

Table 2. Statistical characters of water variations contents in the study waters

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Table 2. Statistical characters of water variations contents in the study waters

Cruise		c_Chla /(μg·L^－1)	c_TSS/(mg·L^－1)	c_ISS /(mg·L^－1)	c_OSS /(mg·L^－1)
THL－201004	Average	12.98±13.88	41.32±35.21	33.36±32.93	7.96±4.57
THL－201004	Range	0.13－46.98	9.90－162.73	7.68－145.60	1.98－19.65
CHL－200910	Average	377.45±761.59	93.45±160.93	23.61±15.16	69.84±153.04
CHL－200910	Range	6.43－3580.27	12.50－942.00	8.50－67.00	2.50－888.00
PYL－201010	Average	8.39±6.25	57.96±31.17	49.93±28.15	8.03±3.41
PYL－201010	Range	1.47－24.65	19.00－168.00	13.00－148.00	3.60－20.00
PRE－201408	Average	1.46±0.79	51.29±40.47	42.71±33.53	8.58±7.31
PRE－201408	Range	0.50－3.33	11.60－191.20	9.53－156.00	2.07－35.20
DYB－201408	Average	3.64±1.88	24.69±9.79	17.02±6.96	7.66±4.26
DYB－201408	Range	1.53－7.94	14.55－44.00	9.75－31.40	3.30－21.00

Table 3. Diffuse attenuation coefficient of water calculated by using the QAA model

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Table 3. Diffuse attenuation coefficient of water calculated by using the QAA model

Step	Parameter	Expression
1	r_rs	R_rs/(0.52＋1.7R_rs)
2	$u (λ) ＝ \frac{b_{b} (λ)}{a (λ) ＋ b_{b} (λ)}$	$\frac{－ g_{0} ＋ {[g_{0}^{2} ＋ 4 g_{1} r_{rs}]}^{\frac{1}{2}}}{2 g_{1}}, g_{0} ＝ 0.089, g_{1} ＝ 0.125$
3	a(555)	$a_{w} (555) ＋ 10^{－ 1.146 － 1.366 χ － 0.469 χ^{2}}, χ ＝ log [\frac{r_{rs} (443) ＋ r_{rs} (488)}{r_{rs} (555) ＋ 5 \frac{r_{rs} (672)}{r_{rs} (488)} r_{rs} (672)}]$
4	B_bp(555)	$\frac{u (555) a (555)}{1 － u (555)} － b_{bw} (555)$
5	η	$\begin{matrix} 2.0 \end{matrix} {1 － 1.2 exp [－ 0.9 \frac{r_{rs} (443)}{r_{rs} (555)}]}$
6	b_bp(λ)	$b_{bp} (555) {(\frac{555}{λ})}^{η}$
7	a(λ)	$\frac{[1 － u (λ)] [b_{bw} (λ) ＋ b_{bp} (λ)]}{u (λ)}$
8	k_d(λ)	(1＋m₀×θ_s)a(λ)＋ $[1 － γ \frac{b_{bw} (λ)}{b_{b} (λ)}]$ ×m₁×1－m₂×exp－m₃×a(λ)b_b(λ)m₀＝0.005, m₁＝4.26, m₂＝0.52, m₃＝10.8, γ＝0.265

Table 4. Comparison of different TSS concentration estimation models from remote sensing

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Table 4. Comparison of different TSS concentration estimation models from remote sensing

Model	Model pattern	R²	r_RMSE /%	X_MAE/(mg·L^－1)
Sigle－band model	－7308.4×R_rs(672)²＋1300.3×R_rs(672)＋29.7	0.19	102.3	32.4
	－128187×R_rs(751)²－1009.6×R_rs(751)＋33.2	0.62	58.5	18.5
	－184397×R_rs(865)²＋621.1×R_rs(865)＋30.1	0.67	51.4	15.3
Band－ratio model	414.4×[R_rs(865)/R_rs(555)]－18.7	0.70	46.8	13.3
Three－band model	791.0×[R_rs(555)＋R_rs(672)]－209.8×[R_rs(488)/R_rs(555)]＋146.5	0.14	107.2	21.6
Our model	21374.0×[R_rs(865)/k_d(555)]－17.8	0.81	39.5	8.8

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Quansheng Lou, Chunchao Zhang, Wei Deng, Wen Su, Guangjia Jiang, Ronghua Ma. Remote Sensing Estimation of Suspended Solids Concentrations in Coastal and Inland Waters Based on Quasi-Analytical Algorithm[J]. Acta Optica Sinica, 2021, 41(19): 1901003

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Paper Information

Category: Atmospheric Optics and Oceanic Optics

Received: Mar. 4, 2021

Accepted: Apr. 23, 2021

Published Online: Oct. 9, 2021

The Author Email: Guangjia Jiang (gjjiang2011@gmail.com), Ronghua Ma (mrhua2002@niglas.ac.cn)

DOI:10.3788/AOS202141.1901003

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