一种湿地河流叶绿素a遥感反演方法

doi:10.12082/dqxxkx.2020.190547

Abstract

Abstract:

Chlorophyll-a (Chl-a) is an important indicator to evaluate water quality security. The accurate estimation of its concentration is of major significance to aquaculture development, aquatic ecosystem sustainability, and human drinking water safety. With the enhancement of the spatial and spectral resolution of earth-observed satellite sensor, remote sensing technology is exerting a growing important effect on monitoring the spatiotemporal changes of water quality in rivers. In this study, we synchronously measured water spectrum and collected water samples along the upper and middle reaches of the Kaidu River and around some small lakes in the Bayanbulak Wetland. Chlorophyll-a concentration and turbidity were measured for each sample in the laboratory. Based on the water reflectance spectrum and measured chlorophyll-a, we initially performed the sensitivity analysis of spectrum band to the concentration of chlorophyll-a, and then established various spectral index models, including band differences, ratios, and difference-sum ratios. Then Chl-a=4.50 mg/m³ was proposed as a hierarchical threshold for dividing waters samples into two groups and 11 empirical and semi-analytical chlorophyll-a retrieval models after calibration were applied to all sample datasets and the two separate datasets with relatively high and low chlorophyll-a concentrations to evaluate their accuracy. The optimal linear relationship between the independent variable (D3B) of three-band semi-analytical model and chlorophyll-a determined that D3B=-0.051 could be regarded as an indicator to classify waters with different chlorophyll-a concentrations. According to the performance of all the models, we ultimately selected the D3B model for high chlorophyll-a concentration waters and the blue-green band ratio model for low chlorophyll-a concentration waters, resulting in the hierarchical retrieval algorithm OC₂-D3B. Its accuracy (R²=0.96, RMSE=0.32 mg/m³, MAE=0.24 mg/m³, and MRE=5.71%) was greatly improved compared with other single algorithms. Finally, we analyzed the spatial distribution and seasonal pattern of chlorophyll-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicate that the chlorophyll-a concentration in lake was higher than that in river, and the highest chlorophyll-a concentration usually appeared in summer, followed by spring and autumn, while the lowest chlorophyll-a concentration occurred in winter. Based on observational data from the Bayanbulak meteorological station, we further analyzed the effects of three environmental factors of temperature, precipitation, and sunshine duration on the chlorophyll-a concentration in the wetland river. The results show that the correlation coefficient between temperature and chlorophyll-a concentration reached 0.88, which was much higher than the other two factors. Thus, it seems that temperature was the main factor affecting chlorophyll-a concentration to some extent. In addition, this study could provide technical support for water environmental protection and water resource regulation in Bayanbulak.

Key words: remote sensing, spectrum analysis, wetland river, model evaluation, chlorophyll-a retrieval, spatiotemporal distribution, meteorological factor, Bayanbulak

LIU Weihua, WANG Siyuan, MA Yuanxu, SHEN Ming, YOU Yongfa, HAI Kai, WU Linlin. A Remote Sensing Method for Retrieving Chlorophyll-a Concentration from River Water Body[J].Journal of Geo-information Science, 2020, 22(10): 2062-2077.DOI:10.12082/dqxxkx.2020.190547

Figures/Tables 18

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Tab.2

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

算法	描述
T_Chl-a^[11]	$R = R rs (433) / R rs (555) × (R rs (412) / R rs (490)) C 0$ $Chl - a = 10 c 1 + c 2 Lo g 10 R + C 3 Lo g 102 R$ $C 0 = - 0.935, C 1 = 0.342, C 2 = - 2.511, C 3 = - 0.277$
OC₂V4^[2]	$R = Lo g 10 (max (R rs 443, R rs 490) / R rs 560)$ $Chl - a = 10 c 0 + c 1 R + c 2 R 2 + c 3 R 3 + c 4 R 4$ $C 0 = 0.2975, C 1 = - 21.502, C 2 = - 215.53, C 3 = - 784.5, C 4 = - 859.7$
OC₄V4^[2]	$R = Lo g 10 (max (R rs 443, R rs 490, R rs 510) / R rs 560)$ $Chl - a = 10 c 0 + c 1 R + c 2 R 2 + c 3 R 3 + c 4 R 4$ $C 0 = - 0.599, C 1 = - 50.54, C 2 = - 578.38, C 3 = - 2525.7, C 4 = - 376.2$
NDCI^[46]	$NDCI = (R rs 708 - R rs 665) / (R rs 708 + R rs 665)$ $Chl - a = 4.0448 + 10.301 × (NDCI)$
FLH^[21]	$FLH = R rs λ 2 - [R rs λ 3 + λ 2 - λ 3 λ 1 - λ 3 * (R rs λ 1 - R rs λ 3)]$ $λ 1 : 665 nm, λ 2 : 681 nm, λ 3 : 708 nm$ $Chl - a = 3.6268 - 11.289 × (FLH) - 17.743 × FLH 2$
MCI^[22]	$MCI = L w (λ 2) - [L w λ 1 + λ 2 - λ 1 λ 3 - λ 1 * (L w λ 3 - L w λ 1)]$ $λ 1 : 681 nm, λ 2 : 708 nm, λ 3 : 753 nm$ $Chl - a = 5.6122 - 2.1844 × (MCI) + 0.6641 × MCI 2$
SCI^[23]	$H Chl = R rs λ 4 + λ 4 - λ 3 λ 4 - λ 2 (R rs λ 2 - R rs λ 4) - R rs λ 3$ $H △ = R rs λ 2 - R rs λ 4 + λ 4 - λ 2 λ 4 - λ 1 (R rs λ 1 - R rs λ 4)$ $SCI = H Chl - H △$ $λ 1 : 560 nm, λ 2 : 620 nm, λ 3 : 665 nm, λ 4 : 681 nm$ $Chl - a = 5.7457 - 7.9685 × (SCI) + 5.7043 × (SCI) 2$
G2B^[14]	$G 2 B = R rs λ 2 R rs λ 1$ $λ 1 : 659 nm, λ 2 : 692 nm$ $Chl - a = 49.739 - 124.14 × G 2 B + 82.754 × G 2 B 2$
D3B^[15]	$D 3 B = R rs λ 1 - 1 - R rs λ 2 - 1 × R rs λ 3$ $λ 1 : 659 nm, λ 2 : 692 nm, λ 3 : 748 nm$ $Chl - a = 6.9756 + 73.431 × D 3 B + 344.53 × D 3 B 2$
L4B^[7]	$L 4 B = R rs λ 1 - 1 - R rs λ 2 - 1 / [R rs λ 4 - 1 - R rs λ 3 - 1]$ $λ 1 : 659 nm, λ 2 : 692 nm, λ 3 : 705 nm, λ 4 : 748 nm$ $Chl - a = 5.5923 + 11.566 × L 4 B + 15.472 × L 4 B 2$
G_Chl-a^[29]	$b b = 1.61 × R rs 779 / (0.082 - 0.6 × R rs 779)$ $Chl - a = (R rs 709 / R rs 665 × 0.7 + b b - 0.4 - b b 1.06) / 0.016$

Tab.2

Fig. 4

Fig. 5

Tab. 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	$x = (R rs 625 - R rs 624) / (R rs 625 + R rs 624) × 100$	$y = 105.97 x 2 + 45.711 x + 8.2691$	0.73

Tab. 3

Fig. 6

Tab. 4

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

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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

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

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

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