<trans-title>A Remote Sensing Method for Retrieving Chlorophyll-a Concentration from River Water Body</trans-title>

Weihua LIU; Siyuan WANG; Yuanxu MA; Ming SHEN; Yongfa YOU; Kai HAI; Linlin WU

doi:10.12082/dqxxkx.2020.190547

Journal of Geo-information Science, Volume. 22, Issue 10, 2062(2020)

A Remote Sensing Method for Retrieving Chlorophyll-a Concentration from River Water Body

Weihua LIU1...2, Siyuan WANG1,*, Yuanxu MA1,2, Ming SHEN1,2, Yongfa YOU1,2, Kai HAI3 and Linlin WU4 |Show fewer author(s)

Author Affiliations

¹Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

²University of Chinese Academy of Sciences, Beijing 100049, China

³Fuzhou University, Fuzhou 350002, China

⁴East China University of Technology, Nanchang 330013, China

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Figures & Tables(18)

Fig. 1. Sampling point location in the Bayanbulak wetland grassland in Xinjiang

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Fig. 2. Remote sensing reflectance above water surface of valid samples

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Fig. 3. Framework for Chl-a estimation based on concentration classification

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Fig. 4. Correlation coefficient between single-band remote sensing reflectance and in situ Chl-a

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Fig. 5. Correlation coefficient between in situ Chl-a and the three kinds of Rrsλ combination

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Fig. 6. Correlation between D3B and measured Chl-a

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Fig. 7. Retrieval errors of the optimal statistical model X6 and 11 existing models

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Fig. 8. In situ Chl-a versus estimated Chl-a of X6, OC₂V4 and D3B in the two level Chl-a datasets

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Fig. 9. Scatterplot of estimated and measured Chl-a value obtained from the leave-one-out procedure

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Fig. 10. In situ Chl-a versus estimated Chl-a of X6, OC₂V4, D3B and OC₂-D3B in the all in situ datasets

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Fig. 11. Spatial distribution of estimated Chl-a using the OC₂V4, D3B, and OC₂-D3B

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Fig. 12. Estimated Chl-a versus in situ Chl-a of matchup samples

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Fig. 13. Temporal variation of monthly average Chl-a concentration in wetland river water bodies from 2016 to 2019 and monthly average Chl-a concentration over the years

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Fig. 14. The variation trend of Chl-a concentration with meteorological factors and correlation analysis

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Table 1. Descriptive statistics of the concentration of water constituents in July 2018

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Table 1. Descriptive statistics of the concentration of water constituents in July 2018

水质参数		最小值	最大值	平均值	标准差	变异系数	采样数
叶绿素a/(mg/m³)	总体	2.53	8.72	4.29	1.65	0.39	38
	干流	2.78	8.06	4.16	1.37	0.33	12
	湖泊	2.53	8.72	5.33	1.98	0.37	13
	支流	2.67	5.24	3.37	0.67	0.20	13
浊度(NTU)	总体	2.57	93.42	34.23	26.56	0.78	38
	干流	4.33	93.42	49.54	29.81	0.60	12
	湖泊	2.57	44.71	25.77	13.92	0.54	13
	支流	2.89	75.38	28.56	16.36	0.57	13

Table 2. Brief descriptions of the 11 types of Chl-a retrieval algorithms used in this study

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Table 2. Brief descriptions of the 11 types of Chl-a retrieval algorithms used in this study

算法	描述
T_Chl-a^[11]	$R = R_{rs} (433) / R_{rs} (555) \times (R_{rs} (412) / R_{rs} {(490))}^{C_{0}}$ $Chl - a = 10^{(c_{1} + c_{2} Lo g_{10} (R) + C_{3} Lo g_{10}^{2} (R))}$ $C_{0} = - 0.935, C_{1} = 0.342, C_{2} = - 2.511, C_{3} = - 0.277$
OC₂V4^[2]	$R = Lo g_{10} (\max (R_{rs} (443), R_{rs} (490)) / R_{rs} (560))$ $Chl - a = 10^{(c_{0} + c_{1} R + c_{2} R^{2} + c_{3} R^{3} + c_{4} R^{4})}$ $\begin{array}{l} C_{0} = 0.2975, C_{1} = - 21.502, C_{2} = - 215.53, C_{3} = - 784.5, \\ C_{4} = - 859.7 \end{array}$
OC₄V4^[2]	$R = Lo g_{10} (\max (R_{rs} (443), R_{rs} (490), R_{rs} (510)) / R_{rs} (560))$ $Chl - a = 10^{(c_{0} + c_{1} R + c_{2} R^{2} + c_{3} R^{3} + c_{4} R^{4})}$ $\begin{array}{l} C_{0} = - 0.599, C_{1} = - 50.54, C_{2} = - 578.38, C_{3} = - 2525.7, \\ C_{4} = - 376.2 \end{array}$
NDCI^[46]	$NDCI = (R_{rs} (708) - R_{rs} (665)) / (R_{rs} (708) + R_{rs} (665))$ $Chl - a = 4.0448 + 10.301 \times (NDCI)$
FLH^[21]	$FLH = R_{rs} (λ_{2}) - [R_{rs} (λ_{3}) + \frac{λ_{2} - λ_{3}}{λ_{1} - λ_{3}} * (R_{rs} (λ_{1}) - R_{rs} (λ_{3}))]$ $λ_{1} : 665 nm, λ_{2} : 681 nm, λ_{3} : 708 nm$ $Chl - a = 3.6268 - 11.289 \times (FLH) - 17.743 \times {(FLH)}^{2}$
MCI^[22]	$MCI = L_{w} (λ_{2}) - [L_{w} (λ_{1}) + \frac{λ_{2} - λ_{1}}{λ_{3} - λ_{1}} * (L_{w} (λ_{3}) - L_{w} (λ_{1}))]$ $λ_{1} : 681 nm, λ_{2} : 708 nm, λ_{3} : 753 nm$ $Chl - a = 5.6122 - 2.1844 \times (MCI) + 0.6641 \times {(MCI)}^{2}$
SCI^[23]	$H_{Chl} = [R_{rs} (λ_{4}) + \frac{λ_{4} - λ_{3}}{λ_{4} - λ_{2}} (R_{rs} (λ_{2}) - R_{rs} (λ_{4}))] - R_{rs} (λ_{3})$ $H_{△} = R_{rs} (λ_{2}) - [R_{rs} (λ_{4}) + \frac{λ_{4} - λ_{2}}{λ_{4} - λ_{1}} (R_{rs} (λ_{1}) - R_{rs} (λ_{4}))]$ $SCI = H_{Chl} - H_{△}$ $λ_{1} : 560 nm, λ_{2} : 620 nm, λ_{3} : 665 nm, λ_{4} : 681 nm$ $Chl - a = 5.7457 - 7.9685 \times (SCI) + 5.7043 \times {(SCI)}^{2}$
G2B^[14]	$G 2 B = \frac{R_{rs} (λ_{2})}{R_{rs} (λ_{1})}$ $λ_{1} : 659 nm, λ_{2} : 692 nm$ $Chl - a = 49.739 - 124.14 \times (G 2 B) + 82.754 \times {(G 2 B)}^{2}$
D3B^[15]	$D 3 B = [R_{rs} {(λ_{1})}^{- 1} - R_{rs} {(λ_{2})}^{- 1}] \times R_{rs} (λ_{3})$ $λ_{1} : 659 nm, λ_{2} : 692 nm, λ_{3} : 748 nm$ $Chl - a = 6.9756 + 73.431 \times (D 3 B) + 344.53 \times {(D 3 B)}^{2}$
L4B^[7]	$L 4 B = [R_{rs} {(λ_{1})}^{- 1} - R_{rs} {(λ_{2})}^{- 1}] / [R_{rs} {(λ_{4})}^{- 1} - R_{rs} {(λ_{3})}^{- 1}]$ $λ_{1} : 659 nm, λ_{2} : 692 nm, λ_{3} : 705 nm, λ_{4} : 748 nm$ $Chl - a = 5.5923 + 11.566 \times (L 4 B) + 15.472 \times {(L 4 B)}^{2}$
G_Chl-a^[29]	$b_{b} = 1.61 \times R_{rs} (779) / (0.082 - 0.6 \times R_{rs} (779))$ $Chl - a = (R_{rs} (709) / R_{rs} (665) \times (0.7 + b_{b}) - 0.4 - b_{b}^{1.06}) / 0.016$

Table 3. The optimal fitting equation between reflectance and Chl-a concentration based on regression analysis

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Table 3. The optimal fitting equation between reflectance and Chl-a concentration based on regression analysis

模型简写	自变量	拟合方程	R²
X1	$x = R_{n} (537)$	$y = 3.2765 x^{2} - 0.3799 x + 3.1175$	0.56
X2	$x = (R_{n} (537) - R_{n} (428)) / (R_{n} (537) + R_{n} (428))$	$y = 14.681 x^{2} + 17.266 x + 8.1809$	0.48
X3	$x = R_{n} (537) / R_{n} (678)$	$y = 8.5277 x^{2} - 0.2788 x + 3.3603$	0.36
X4	$x = (R_{n} (537) - R_{n} (678)) / (R_{n} (537) + R_{n} (678))$	$y = 8.6092 x^{2} + 14.272 x + 9.3707$	0.34
X5	$x = (R_{rs} (384) - R_{rs} (385)) * 100$	$y = 75435 x^{2} + 1063.9 x + 6.896$	0.63
X6	$x = R_{rs} (689) / R_{rs} (613)$	$y = 107.82 x^{2} - 166.85 x + 67.757$	0.82
X7	$x = (R_{rs} (625) - R_{rs} (624)) / (R_{rs} (625) + R_{rs} (624)) \times 100$	$y = 105.97 x^{2} + 45.711 x + 8.2691$	0.73

Table 4. Descriptive statistics of the water samples with D3B=-0.051 and Chl-a=4.5 mg/m³as hierarchical threshold

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Table 4. Descriptive statistics of the water samples with D3B=-0.051 and Chl-a=4.5 mg/m³as hierarchical threshold

D3B分组	Chl-a最小值	Chl-a最大值	Chl-a均值	标准差	变异系数	采样点数
D3B≤-0.051	2.53	4.25	3.40	0.50	0.15	24
D3B>-0.051	2.67	8.72	6.05	1.73	0.28	12
Chl-a分组	D3B最小值	D3B最大值	D3B均值	标准差	变异系数	采样点数
Chl-a ≤4.50 mg/m³	-0.092	-0.041	-0.072	0.014	-0.19	26
Chl-a>4.50 mg/m³	-0.047	0.026	-0.014	0.023	-1.69	10

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Weihua LIU, Siyuan WANG, Yuanxu MA, Ming SHEN, Yongfa YOU, Kai HAI, Linlin WU. A Remote Sensing Method for Retrieving Chlorophyll-a Concentration from River Water Body[J]. Journal of Geo-information Science, 2020, 22(10): 2062

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Paper Information

Received: Sep. 25, 2019

Accepted: --

Published Online: Apr. 23, 2021

The Author Email: WANG Siyuan (wangsy@radi.ac.cn)

DOI:10.12082/dqxxkx.2020.190547

Topics

Resource Economy

Research Articles

Table 1. Descriptive statistics of the concentration of water constituents in July 2018

Table 1. Descriptive statistics of the concentration of water constituents in July 2018

Table 2. Brief descriptions of the 11 types of Chl-a retrieval algorithms used in this study

Table 2. Brief descriptions of the 11 types of Chl-a retrieval algorithms used in this study

Table 3. The optimal fitting equation between reflectance and Chl-a concentration based on regression analysis

Table 3. The optimal fitting equation between reflectance and Chl-a concentration based on regression analysis

Table 4. Descriptive statistics of the water samples with D3B=-0.051 and Chl-a=4.5 mg/m3as hierarchical threshold

Table 4. Descriptive statistics of the water samples with D3B=-0.051 and Chl-a=4.5 mg/m3as hierarchical threshold

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Table 4. Descriptive statistics of the water samples with D3B=-0.051 and Chl-a=4.5 mg/m³as hierarchical threshold

Table 4. Descriptive statistics of the water samples with D3B=-0.051 and Chl-a=4.5 mg/m³as hierarchical threshold