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Water Quality Satellite Remote Sensing Monitoring Model of Fujian Coastland Based on Fuzzy Evaluation
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摘要: 卫星遥感技术对于开展连续、实时、大尺度的海洋水质状况监测具有明显优势。该文充分利用卫星遥感的标准算法模型和半分析算法模型分别反演得到叶绿素a、颗粒状有机碳、黄色物质和透明度等海洋生态参数，并选取与福建沿海海水受污染程度密切相关的溶解氧、化学耗氧量、无机氮和活性磷酸盐作为海洋水质的评价因子，通过建立海洋生态参数与海洋水质评价因子两者之间的统计关系模型，在海洋水质综合评价中引入模糊综合评价法，最终建立一套基于卫星遥感和模糊评价的海洋水质监测模型，并利用2009—2013年福建沿海同步获取的海洋水质现场实测数据对模型的反演精度进行验证。结果表明：使用该监测模型开展对福建沿海海洋水质卫星遥感监测是可行的，监测准确率为81%，具有较好的业务化应用前景，由于模型对于Ⅳ类海洋水质监测的准确率明显高于Ⅱ类和Ⅲ类海洋水质，因此，比较适合于福建近岸海域的海洋水质监测。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.
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图 2 海洋水质评价因子统计模型精度
Fig. 2 The precision of marine water quality evaluation factor model
图 3 海洋水质卫星遥感监测结果
Fig. 3 Results of marine water quality of satellite remote sensing monitoring
表 1 MODIS应用于海洋水色监测的可见光探测通道
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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表 2 福建沿海水质因子观测站地理位置
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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表 3 海洋水质评价因子的评价等级 (单位：mg·L-1)
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 (FDO) FDO≥6 6＞FDO≥5 5＞FDO≥4 FDO＜4 化学耗氧量 (FCOD) FCOD≤2 2＜FCOD≤3 3＜FCOD≤4 FCOD＞4 无机氮 (FDIN) FDIN≤0.2 0.2＜FDIN≤0.3 0.3＜FDIN≤0.4 FDIN＞0.4 活性磷酸盐 (FPO4-P) FPO4-P≤0.015 0.015＜FPO4-P≤0.03 0.015＜FPO4-P≤0.03 FPO4-P＞0.03
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表 4 海洋水质评价因子统计模型精度
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 (FDO) 13.0 0.9602 0.680 y=5.323+0.254x 化学耗氧量 (FCOD) 18.2 0.1651 0.885 y=0.167+0.797x 无机氮 (FDIN) 20.5 0.2433 0.587 y=0.215+0.253x 活性磷酸盐 (FPO4-P) 31.4 0.0116 0.754 y=0.007+0.771x
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表 5 现场观测与遥感监测海洋水质结果对比
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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表 6 海洋水质遥感监测准确率
Table 6 The accuracy of marine water quality monitoring
地面实测海洋水质 Ⅱ类 Ⅲ类 Ⅳ类 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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[1] 福建省海洋与渔业厅. 2013年福建省海洋环境状况公报. 2014. [2] 朱光文.海洋监测技术的国内外现状及发展趋势.气象水文海洋仪器, 1997(2):1-14. http://www.cnki.com.cn/Article/CJFDTOTAL-QXSW199702000.htm [3] 徐鸿楷.半集均方差水质评价模式.环境科学, 1985, 6(4):55-57. http://www.cnki.com.cn/Article/CJFDTOTAL-HJKZ198504012.htm [4] 刘广吉, 刘淑文.灰色聚类法在水质评价中的应用.水利水电技术, 1988(6):1-4. http://www.cnki.com.cn/Article/CJFDTOTAL-KJXX200911012.htm [5] 冯玉国.物元分析在地下水水质评价中的应用.水文, 1995, 15(1):54-56. http://www.cnki.com.cn/Article/CJFDTOTAL-SWZZ501.011.htm [6] 刘国东, 黄川友, 丁晶.水质综合评价的人工神经网络模型.中国环境科学, 1998, 18(6):514-517. http://www.cnki.com.cn/Article/CJFDTOTAL-QHHJ199701007.htm [7] 金菊良, 魏一鸣, 丁晶.水质综合评价的投影寻踪模型.环境科学学报, 2001, 21(4):431-434. http://www.cnki.com.cn/Article/CJFDTOTAL-YJGX201503010.htm [8] 张景平, 黄小平, 江志坚, 等.珠江口海域污染的水质综合污染指数和生物多样性指数评价.热带海洋学报, 2010, 29(1):69-73. doi: 10.11978/j.issn.1009-5470.2010.01.069 [9] 曹宇峰, 林春梅, 孙霞.模糊数学法在海洋水质评价中的应用.海洋技术, 2011, 30(2):118-122. http://www.cnki.com.cn/Article/CJFDTOTAL-ZXDB201019112.htm [10] 柯丽娜, 王权明, 孙新国, 等.基于可变模糊识别模型的海水环境质量评价.生态学报, 2013, 33(6):1889-1899. http://www.cnki.com.cn/Article/CJFDTOTAL-STXB201306024.htm [11] 孔庆瑜, 陈远策.武昌东湖水体污染的航空遥感分析.湖北大学学报:自然科学版, 1990, 12(1):83-88. http://www.cnki.com.cn/Article/CJFDTOTAL-HDZK199001017.htm [12] 傅江, 季耿善.彩色红外航片用于水污染遥感监测的定量分析.中国环境科学, 1994, 14(6):416-421. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGHJ406.005.htm [13] 疏小舟, 汪骏发, 沈鸣明, 等.航空成像光谱水质遥感研究.红外与毫米波学报, 2000, 8(4):273-276. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYH200004004.htm [14] 李旭文, 季耿善, 杨静.苏州运河水质的TM分析.环境遥感, 1993, 8(1):36-44. http://www.cnki.com.cn/Article/CJFDTOTAL-YGXB199301003.htm [15] 陆家驹.长江南京江段水质遥感分析.国土资源遥感, 2002(3):33-36. doi: 10.6046/gtzyyg.2002.03.09 [16] 吴敏, 王学军.应用MODIS遥感数据监测巢湖水质.湖泊科学, 2005, 17(2):110-113. http://www.cnki.com.cn/Article/CJFDTOTAL-FLKX200502004.htm [17] 吴炳芳, 史震古.遥感技术在水质监测中的应用.江西水利科技, 1990(2):83-88. http://www.cnki.com.cn/Article/CJFDTOTAL-NCJI200704056.htm [18] 佘丰宁, 蔡启铭.主成分监督分类及其在水质特征遥感图像识别中的应用.湖泊科学, 1997, 9(3):261-268. doi: 10.18307/1997.0311 [19] 马跃良, 王云鹏, 贾桂梅.珠江广州河段水体污染的遥感监测应用研究.重庆环境科学, 2003, 25(3):13-16. http://www.cnki.com.cn/Article/CJFDTOTAL-CQHJ200303005.htm [20] 邬明权, 牛铮, 高帅, 等.渤海陆源入海排污口的多尺度遥感监测分析.地球信息科学学报, 2012, 14(3):405-410. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201203019.htm [21] 李云波, 张永刚, 唐海川, 等.基于海气通量算法的海上蒸发波导诊断模型.应用气象学报, 2009, 20(5):628-633. doi: 10.11898/1001-7313.20090515 [22] 陈钻, 李海胜.新型台风海洋网络气象信息系统的设计与实现.应用气象学报, 2012, 23(2):245-250. doi: 10.11898/1001-7313.20120214 [23] 柯宗建, 华丽娟, 钟霖浩, 等.海温异常对东亚夏季风强度先兆信号的影响.应用气象学报, 2015, 26(5):536-544. doi: 10.11898/1001-7313.20150503 [24] 张春桂, 蔡义勇, 张加春.MODIS遥感数据在我国台湾海峡海雾监测中的应用.应用气象学报, 2009, 20(1):8-16. doi: 10.11898/1001-7313.20090102 [25] 黄健, 黄辉军, 黄敏辉, 等.广东沿岸海雾决策树预报模型.应用气象学报, 2011, 22(1):107-114. doi: 10.11898/1001-7313.20110111 [26] 国家技术监督局.GB 3097-1997海水水质标准.北京:中国标准出版社, 2003. [27] Du C, Shang S, Dong Q, et al.Characteristics of chromophoric dissolved organic matter in the nearshore waters of the western Taiwan Strait.Estuarine Coastal and Shelf Science, 2010, 88:350-356. doi: 10.1016/j.ecss.2010.04.014 [28] Guo W, Stedmon C, Han Y, et al.The conservative and non-conservative behavior of chromophoric dissolved organic matter in Chinese estuarine waters.Marine Chemistry, 2007, 107:357-366. doi: 10.1016/j.marchem.2007.03.006 [29] Dong Q, Shang S, Lee Z.An algorithm to retrieve absorption coefficient of chromophoric dissolved organic matter from ocean color.Remote Sensing of Environment, 2013, 28:259-267. http://www.doc88.com/p-9119574077064.html [30] Zhu W, Yu Q, Tian Y Q, et al.Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above-surface hyperspectral remote sensing.J Geophys Res, 2011, 116, doi: 10.1029/2010JC006523. [31] Mannino A, Russ M E, Hooker S B.Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the US Middle Atlantic Bight.J Geophys Res, 2008, 113, doi: 10.1029/2007JC004493. [32] Lee Z, Carder K L, Arnone R A.Deriving inherent optical properties from water color:A multiband quasi-analytical algorithm for optically deep waters.Applied Optics, 2002, 41:5755-5772. doi: 10.1364/AO.41.005755 [33] 张春桂, 曾银东, 张星, 等.海洋叶绿素a浓度反演及其在赤潮监测中的应用.应用气象学报, 2007, 18(6):821-831. doi: 10.11898/1001-7313.200706124 [34] 曾银东, 商少凌, 张彩云, 等.南海东北部基于标准经验算法的遥感叶绿素a反演结果比较分析.海洋科学, 2004, 28(8):14-18. http://www.cnki.com.cn/Article/CJFDTOTAL-HYKX200408003.htm [35] Stramski D, Reynolds R A, Kahru M, et al.Estimation of particulate organic carbon in the ocean from satellite remote sensing.Science, 1999, 285:239-242. doi: 10.1126/science.285.5425.239 [36] Stramski D, Reynolds R A, Babin M, et al.Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans.Biogeosciences, 2008, 5:171-201. doi: 10.5194/bg-5-171-2008 -
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