Water Quality Satellite Remote Sensing Monitoring Model of Fujian Coastland Based on Fuzzy Evaluation

Zhang Chungui; Zeng Yindong; Ma Zhiguo

doi:10.11898/1001-7313.20160112

Vol.27, NO.1, 2016

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Article Navigation > Journal of Applied Meteorological Science > 2016 > 27(1): 112-122

Zhang Chungui, Zeng Yindong, Ma Zhiguo. Water quality satellite remote sensing monitoring model of Fujian coastland based on fuzzy evaluation. J Appl Meteor Sci, 2016, 27(1): 112-122. DOI: 10.11898/1001-7313.20160112.

Citation:

Zhang Chungui, Zeng Yindong, Ma Zhiguo. Water quality satellite remote sensing monitoring model of Fujian coastland based on fuzzy evaluation. J Appl Meteor Sci, 2016, 27(1): 112-122. DOI: 10.11898/1001-7313.20160112.

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Water Quality Satellite Remote Sensing Monitoring Model of Fujian Coastland Based on Fuzzy Evaluation

DOI: 10.11898/1001-7313.20160112

1.
Fujian Institute of Meteorological Science, Fuzhou 350001
2.
Fujian Marine Forecasts, Fuzhou 350003

Received Date: 2015-04-16
Rev Recd Date: 2015-12-03
Publish Date: 2016-01-31

Abstract

Abstract

As the development of satellite remote sensing technology of the high resolution and multi-survey spectrum channel, it brings an obvious advantage for ocean monitoring of water quality situation for continuous, real-time, large scale. With the observed samples of 12 marine water quality monitoring sites in the marine sector in Fujian coastal water, the remote sensing satellite standard algorithm and semi-analysis algorithm model is adopted, and marine ecological parameters are retrieved such as chlorophyll-a, particulate organic carbon, yellow substance and transparency. Evaluation factors of sea water closely related to the marine pollution degree in Fujian coastland are selected as dissolved oxygen, chemical oxygen demand, inorganic nitrogen and active phosphate, the statistical relationship model is established between marine ecology parameters and the evaluation factor of marine water quality. The evaluation of sea water quality is conducted by the fuzzy comprehensive evaluation method, and finally a set of marine water quality monitoring model is finished based on the satellite remote sensing and fuzzy evaluation model. The inversion precision of the model is verified by the synchronization acquisition field measurement of the marine water quality of Fujian coastal water in 2009-2013. Results show that, for the computed yellow substance by semi-analytical algorithm, the average relative error is 34%, the root mean square error is 0.179; for the computed transparency the average relative error is 26%, the root mean square error is 0.332, so the inversion result is ideal. For dissolved oxygen, the average relative error of the statistical model is the minimum, while the active phosphate model is the maximum, all four models have good agreement with measured values, and the correlation coefficient of the regression equation is more than 0.58. Results of the satellite remote sensing monitoring show that in most of the harbor and rivers into the sea along the coast of Fujian, the water quality barely meets class Ⅳ standard, the ocean water quality gradually improves with the offshore distance, and the harbor pollution trend is greater inside bay than that of the outside. From 304 samples of marine water quality monitoring, the satellite monitoring results of 245 samples are consistent with the ground observations, and the accurate rate of monitoring is 81%. The use of the monitoring model is feasible for the satellite remote sensing monitoring of sea water quality in Fujian coastland, so the prospect of business application is better. Because the accurate rate of the model is obviously higher to the marine water quality monitoring of Ⅳ than that of Ⅱ and Ⅲ , the marine water quality monitoring model is more suitable for the offshore sea area in Fujian.
- satellite remote sensing;
- fuzzy evaluation;
- water quality;
- Fujian coastland

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References(36)

Figures and Tables

Fig. 1 Frame of the marine water quality monitoring technology

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Fig. 2 The precision of marine water quality evaluation factor model

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Fig. 3 Results of marine water quality of satellite remote sensing monitoring

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Table 1 Visible light detection channel applied to ocean color monitoring by MODIS

通道	带宽/μm	中心波长/nm	波段	分辨率/m	主要用途
8	0.405~0.420	412	蓝光	1000	海洋水色、浮游生物
9	0.438~0.448	443	蓝光	1000	海洋水色、浮游生物
10	0.483~0.493	488	蓝光	1000	海洋水色、浮游生物
12	0.546~0.556	547	绿光	1000	海洋水色、浮游生物
13	0.662~0.672	667	红光	1000	海洋水色、浮游生物

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Table 2 The geographical location of ocean quality factor observation stations in Fujian Coastland

海域名称	站点编号	纬度	经度
三沙湾	SS08	26.715°N	119.776°E
	SS17	26.627°N	119.727°E
	SS24	26.571°N	119.814°E
罗源湾	LY01	26.470°N	119.646°E
	LY10	26.390°N	119.714°E
	LY17	26.435°N	119.829°E
闽江口	MJ03	26.177°N	119.651°E
	MJ08	26.137°N	119.611°E
	MJ13	26.058°N	119.675°E
福清湾	FQ01	25.642°N	119.502°E
	FQ07	25.591°N	119.545°E
	FQ12	25.665°N	119.614°E
海坛岛	HT01	25.646°N	119.620°E
	HT06	25.430°N	119.676°E
	HT10	25.504°N	119.839°E
兴化湾	XH01	25.428°N	119.200°E
	XH09	25.395°N	119.379°E
	XH19	25.325°N	119.447°E
湄洲湾	MZ01	25.234°N	118.966°E
	MZ08	25.121°N	118.987°E
	MZ17	25.048°N	119.077°E
泉州湾	QZ01	24.850°N	118.643°E
	QZ07	24.842°N	118.730°E
	QZ15	24.774°N	118.800°E
深沪湾	SH01	24.660°N	118.662°E
	SH05	24.663°N	118.676°E
	SH10	24.633°N	118.691°E
厦门湾	XM01	24.581°N	118.173°E
	XM04	24.411°N	118.148°E
	XM07	24.508°N	118.280°E
旧镇湾	JZ01	24.000°N	117.740°E
	JZ05	23.938°N	117.720°E
	JZ08	23.943°N	117.706°E
东山湾	DS01	23.883°N	117.523°E
	DS09	23.751°N	117.442°E
	DS15	23.740°N	117.578°E

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Table 3 Evaluation factor grade of marine water quality assessment (unit: mg·L^-1)

评价因子观测值	Ⅰ类	Ⅱ类	Ⅲ类	Ⅳ类
溶解氧 (F_DO)	F_DO≥6	6＞F_DO≥5	5＞F_DO≥4	F_DO＜4
化学耗氧量 (F_COD)	F_COD≤2	2＜F_COD≤3	3＜F_COD≤4	F_COD＞4
无机氮 (F_DIN)	F_DIN≤0.2	0.2＜F_DIN≤0.3	0.3＜F_DIN≤0.4	F_DIN＞0.4
活性磷酸盐 (F_PO4-P)	F_PO4-P≤0.015	0.015＜F_PO4-P≤0.03	0.015＜F_PO4-P≤0.03	F_PO4-P＞0.03

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Table 4 The precision of marine water quality evaluation factor model

统计模型	平均相对误差/%	均方根误差	相关系数	检验方程
溶解氧 (F_DO)	13.0	0.9602	0.680	y=5.323+0.254x
化学耗氧量 (F_COD)	18.2	0.1651	0.885	y=0.167+0.797x
无机氮 (F_DIN)	20.5	0.2433	0.587	y=0.215+0.253x
活性磷酸盐 (F_PO4-P)	31.4	0.0116	0.754	y=0.007+0.771x

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Table 5 Comparison of field observation and remote sensing monitoring of marine water quality

日期	海域名称	站点编号	现场观测海洋水质	遥感监测海洋水质
2009-05-10	三沙湾	SS10	Ⅳ	Ⅳ
2009-05-11	闽江口	MJ06	Ⅱ	Ⅲ
2009-05-12	福清湾	FQ05	Ⅳ	Ⅳ
2009-11-25	兴化湾	XH06	Ⅲ	Ⅲ
2009-11-02	厦门湾	XM02	Ⅲ	Ⅲ
2009-08-20	东山湾	DS13	Ⅱ	Ⅲ
2010-11-12	罗源湾	LY10	Ⅳ	Ⅳ
2010-11-10	闽江口	MJ01	Ⅳ	Ⅳ
2010-08-11	福清湾	FQ10	Ⅲ	Ⅱ
2010-08-12	深沪湾	SH07	Ⅱ	Ⅱ
2010-01-13	厦门湾	XM02	Ⅲ	Ⅳ
2010-11-07	东山湾	DS09	Ⅳ	Ⅳ
2011-08-15	三沙湾	SS08	Ⅳ	Ⅳ
2011-08-03	闽江口	MJ13	Ⅳ	Ⅳ
2011-08-19	兴化湾	XH04	Ⅲ	Ⅱ
2011-08-16	泉州湾	QZ07	Ⅳ	Ⅳ
2011-08-18	深沪湾	SH10	Ⅱ	Ⅱ
2011-08-18	东山湾	DS03	Ⅲ	Ⅲ
2012-11-14	三沙湾	SS18	Ⅳ	Ⅳ
2012-08-16	罗源湾	LY06	Ⅲ	Ⅲ
2012-08-21	闽江口	MJ04	Ⅱ	Ⅲ
2012-08-14	兴化湾	XH10	Ⅲ	Ⅳ
2012-08-13	湄洲湾	MZ03	Ⅱ	Ⅱ
2012-05-23	泉州湾	QZ09	Ⅳ	Ⅳ
2012-08-13	旧镇湾	JZ03	Ⅲ	Ⅳ
2013-08-07	福清湾	FQ02	Ⅲ	Ⅳ
2013-01-22	平潭岛	PT10	Ⅲ	Ⅲ
2013-08-07	泉州湾	QZ03	Ⅳ	Ⅳ
2013-11-24	深沪湾	SH03	Ⅲ	Ⅳ
2013-01-16	东山湾	DS04	Ⅲ	Ⅲ

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Table 6 The accuracy of marine water quality monitoring

地面实测海洋水质	Ⅱ类		Ⅲ类		Ⅳ类
地面实测海洋水质	样本量	百分比/%	样本量	百分比/%	样本量	百分比/%
Ⅱ类 (样本量为51)	31	61	13	25	7	14
Ⅲ类 (样本量为87)	12	14	52	60	23	26
Ⅳ类 (样本量为166)	2	1	2	1	162	98

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