一种湿地河流叶绿素a遥感反演方法

doi:10.12082/dqxxkx.2020.190547

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (10): 2062-2077.doi: 10.12082/dqxxkx.2020.190547

一种湿地河流叶绿素a遥感反演方法

刘卫华¹^,²(), 王思远¹^,^*(), 马元旭¹^,², 申明¹^,², 游永发¹^,², 海凯³, 吴林霖⁴

1.中国科学院空天信息创新研究院,数字地球重点实验室,北京 100094
2.中国科学院大学,北京 100049
3.福州大学,福州 350002
4.东华理工大学,南昌 330013

收稿日期:2019-09-25 修回日期:2019-12-11 出版日期:2020-10-25 发布日期:2020-12-25
通讯作者: 王思远 E-mail:liuwh@radi.ac.cn;wangsy@radi.ac.cn
作者简介:刘卫华（1993— ）,男,河南周口人,硕士生,主要从事水色和生态遥感研究。E-mail:liuwh@radi.ac.cn
基金资助:
中国科学院战略性先导科技专项(A类)地球大数据科学工程(XDA19030501);第二次青藏高原综合科学考察专题(2019QZKK0204)

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

LIU Weihua¹^,²(), WANG Siyuan¹^,^*(), MA Yuanxu¹^,², SHEN Ming¹^,², YOU Yongfa¹^,², HAI Kai³, WU Linlin⁴

1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Fuzhou University, Fuzhou 350002, China
4. East China University of Technology, Nanchang 330013, China

Received:2019-09-25 Revised:2019-12-11 Online:2020-10-25 Published:2020-12-25
Contact: WANG Siyuan E-mail:liuwh@radi.ac.cn;wangsy@radi.ac.cn
Supported by:
The Strategic Priority Research Program of the Chinese Academy of Sciences: CAS Earth Big Data Science Project(XDA19030501);The Second Comprehensive Scientific Investigation of the Tibetan Plateau(2019QZKK0204)

摘要/Abstract

摘要：

叶绿素a作为一项重要的水质安全评价指标,其浓度的准确监测对水产行业发展、水生态系统平衡和人类饮水安全等有着重要意义。随着对地观测卫星传感器空间和光谱分辨率的提高,遥感技术在河流水质时空变化监测中发挥着越来越重要的作用。本文以新疆巴音布鲁克湿地河流水体为研究对象,同步采集了水体反射光谱和水样,并在实验室对叶绿素a、浊度等水质参数进行测定。首先,基于光谱波段对叶绿素a浓度的敏感性分析,构建了多种光谱指数模型;然后,提出以4.50 mg/m³作为水体叶绿素a浓度分级阈值,利用三波段半分析模型因子D3B与叶绿素a的线性关系建立水体叶绿素a浓度分级标准,进而对比评估了11种经验、半分析模型分别在全部样本数据集和两级叶绿素a浓度数据集中的精度表现;其次,根据各模型精度结果选用三波段半分析模型D3B和蓝绿波段比模型OC₂V4,组成叶绿素a分级反演算法OC₂-D3B,其精度（R²=0.96,RMSE=0.32 mg/m³,MAE=0.24 mg/m³,MRE=5.71%）相比以上2种单一算法提高了50%以上;最后,本文利用Sentinel-2影像,对湿地河流水体叶绿素a浓度的空间分布特征和季节时序模式进行了分析,得到该水域夏季叶绿素a含量最高,春秋季次之,冬季最低的结论。此外,本研究还发现气温相比其他环境因子对水体Chl-a浓度的控制作用更加明显。

关键词: 遥感, 光谱分析, 湿地河流, 模型评价, 叶绿素a反演, 时空分布, 气象因子, 巴音布鲁克

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

刘卫华, 王思远, 马元旭, 申明, 游永发, 海凯, 吴林霖. 一种湿地河流叶绿素a遥感反演方法[J]. 地球信息科学学报, 2020, 22(10): 2062-2077.DOI:10.12082/dqxxkx.2020.190547

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

图/表 18

图1

表1

图2

图3

表2

本研究采用的11种Chl-a反演算法的简要描述"

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]	$b b = 1.61 × R rs 779 / (0.082 - 0.6 × R rs 779)$ $Chl - a = (R rs 709 / R rs 665 × 0.7 + b b - 0.4 - b b 1.06) / 0.016$

表2

图4

图5

表3

基于回归分析的水体反射率与Chl-a浓度的最佳拟合方程"

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

表3

图6

表4

图7

图8

图9

图10

图11

图12

图13

图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

一种湿地河流叶绿素a遥感反演方法

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

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