地球科学进展 ›› 2009, Vol. 24 ›› Issue (2): 150 -158. doi: 10.11867/j.issn.1001-8166.2009.02.0150

综述与评述 上一篇    下一篇

内陆水质遥感不确定性:问题综述
周冠华 1,2,唐军武 3,田国良 4,李 京 2,柳钦火 4   
  1. 1.北京师范大学资源学院,北京 100875;   2.民政部/教育部减灾与应急管理研究院, 北京 100875;3.国家卫星海洋应用中心,北京 100081;
    4.中国科学院遥感应用研究所,遥感科学国家重点实验室, 北京 100101  
  • 收稿日期:2008-07-15 修回日期:2008-12-25 出版日期:2009-02-10
  • 通讯作者: 周冠华 E-mail:zhouguanhua@163.com
  • 基金资助:

    国家科技支撑计划课题“基于环境一号等国产卫星的环境遥感监测关键技术研究”(编号:2008BAC34B03);国家重点基础研究发展计划项目“陆表生态环境要素主被动遥感协同反演理论与方法”(编号:2007CB714403) 联合资助.

Uncertainty Analysis of Inland Water Quality Remote Sensing: A Review

Zhou Guanhua 1,2,Tang Junwu 3,Tian Guoliang 4,Li Jing 2,Liu Qinhuo 4   

  1. 1.College of Resources Sciences and Technology, Beijing Normal University, Beijing 100875,China;
    2.Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs & Ministry of Education, Beijing 100875; 3.National Satellite Ocean Application Service,Beijing 100081, China;4. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2008-07-15 Revised:2008-12-25 Online:2009-02-10 Published:2009-02-10

内陆水质遥感是目前定量遥感领域的热点与难点,多种不确定性因素影响了内陆水质遥感的研究进展。较系统地总结了国内外近年来内陆水质遥感监测存在的突出问题与影响水质遥感监测精度的关键因素,具体从内陆水体光学特性的复杂性、大气校正的复杂性与水面反射光校正的不确定性、反演算法与生物光学模型的复杂性与区域性、现有传感器监测内陆水质在性能方面的局限性以及影响因素的复杂性与综合性几个方面进行了阐述,并指出了内陆水质遥感监测的研究重点与发展方向。

Water quality remote sensing is the hot and difficult topic in the field of environment remote sensing. Various factors affect the improvement of inland water quality remote sensing precision. This paper comments on the outstanding problems existing in inland water quality remote sensing monitoring and some key factors influencing the water quality parameters information extraction precision both at home and abroad. The complexity of the optical characteristics of inland body embodies the independence of the water body components, the similarity of the characteristic spectrum and the coupling effects of water components signal and together with the temporal and spatial variation of the water optics. The uncertainty of atmosphere correction embodies the variation of atmospheric aerosol optical characteristics and the uncertainty of correction of reflected light from wave water surface. The complexity and regional characteristics of bio-optical models demand complex inversion algorithms. The scale effects and uncertainty of model validation are obvious for multi-source remote sensing data cooperative inversion. Inland water quality remote sensing differs from ocean color remote sensing, which demands remoted data with high spatial resolution, spectral resolution and temporal resolution. Therefore, the performance limitations of the available satellite sensors in inland water quality monitoring were discussed in detail. All the above-mentioned factors were complicated and integrated. The emphases and the development direction of inland water quality remote sensing were pointed out.

中图分类号: 

[1] Tang Junwu. The simulation of marine optical properties and color sensing models[D].Beijing: Graduate University of Chinese Academy of Sciences (Institute of Remote Sensing Application),1999.[唐军武. 海洋光学特性模拟与遥感模型[D].北京:中国科学院研究生院(遥感应用研究所), 1999.]
[2] Falkowski P G. The role of phytoplankton photosynthesis in global biogeochemical cycles[J].Photosynthesis Research,1994,39: 235-258.
[3] Luoma S N. Can we determine the biological availability of sedimentbound trace elements?[J].Hydrobiologia, 1989,176/177(1): 379-396.
[4] Siegel D A, Maritorena S, Nelson N B. Global distribution and dynamics of colored dissolved and detrital organic materials[J].Journal of Geophysical Research,2002,107(C12):3288.
[5] Mueller J L, Fargion G S, McClain C R, et al. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume IV: Inherent Optical Properties: Instruments, Characterizations, Field Measurements and Data Analysis Protocols[R]. NASA/TM-2003-211621/Rev4-Vol.IV,2003.
[6] IOCCG. Remote sensing of ocean colour in coastal, and other optically-complex, waters[C]//Sathyendranath S, ed. Reports of the International Ocean-Colour Coordinating Group,No.3, IOCCG, Dartmouth, Canada,2000.
[7] Li Sihai. Principal and Application of Ocean Color Remote Sensing[M]. Beijing: Ocean Press,2002.[李四海.海洋水色遥感原理与应用[M]. 北京:海洋出版社,2002.]
[8] Bukata R P, Jerome J H, Kondratyev K Y,et al. Optical Properties and Remote Sensing of Inland and Coastal Waters[M]. Boca Raton: CRC Press,1995.
[9] Zhou Guanhua. Research of simulation of water optical properties and remote sensing inversion of inland water quality parameter[D]. Beijing: Graduate University of Chinese Academy of Sciences (Institute of Remote Sensing Application),2007.[周冠华. 内陆水体光学特性模拟与水质遥感反演研究[D].北京:中国科学院研究生院(遥感应用研究所),2007.]
[10] Hunter P D, Tyler A N, Présing M, et al. Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution[J].Remote Sensing of Environment,2008,112:1 527-1 544.
[11] Ma Chaofei, Jiang Xingwei, Tang Junwu, et al. Inverse algorithms of ocean constituents for HY-1/CCD broadband data[J].Acta Oceanologica Sinica, 2005,27(4):38-44.[马超飞,蒋兴伟,唐军武,等. HY-1CCD宽波段水色要素反演算法[J]. 海洋学报,2005,27(4):38-44.]
[12] Bricaud Annick, Babin Marcel, Morel Andre, et al. Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton:analysis and parameterization[J]Journal of Geophysical Research,1995, 100(c7): 13 321-13 332.
[13] Ma R, Tang J, Dai J, et al. Absorption and scattering properties of water body in Taihu lake, China: Absorption[J].International Journal of Remote Sensing,2006, 27(19): 4 277-4 304.
[14] Li Jing. A study on determination of concentration of suspended solids in water by remote sensing[J].Acta Sientiae Circumstantiae,1986, 6(2): 166-173.[李京.水域悬浮固体含量的遥感定量研究[J].环境科学学报,1986, 6(2): 166-173.]
[15] Richardson L L, LeDREW E F. Remote Sensing of Aquatic Coastal Ecosystem Processes: Science and Management Applications[M]. Netherlands: Springer,2006.
[16] Paerl H W. Nuisance phytoplankton blooms in coastal, estuarine, and inland waters[J].Limnology and Oceanography,1988,33:823-847.
[17] Andre J M. Ocean color remote-sensing and the subsurface vertical structure of phytoplankton pigments[J]. Deep Sea Research,1992,39: 763-779.
[18] Kirk J T O. Light & Photosynthesis in Aquatic Ecosystems[M]. Cambridge: Cambridge University Press,1994.
[19] Liu Quanhua, Carter Corinne. Atmospheric correction over ocean for production of remote sensing reflectance intermediate product[R].Visible/infrared imager/radiometer suite algorithm theoretical basis document version5, 2002.
[20] Pan Delu, He Xianqiang, Li Shujing, et al. Study on application potentiality of the first China’s ocean satellite HY-1A[J].Acta Oceanologica Sinica, 2004,26(2):37-44.[潘德炉,何贤强,李淑菁,等. 我国第一颗海洋卫星 HY-1A的应用潜力研究[J].海洋学报, 2004,26(2):37-44.]
[21] Wang Haijun. Study on atmospheric correction methods for water color in Taihu lake[D]. Nanjing: Nanjing Normal University,2007.[王海军.太湖水色遥感大气校正方法研究[D]. 南京:南京师范大学,2007.]
[22] Olszewski Jerzy, Piotr Kowalczuk. Sky glint correction in measurements of upward radiance above the sea surface[J].Oeanologia,2000,42(2): 251-262.
[23] Ji Gengshan, Yang Jing, Fu Jiang. A study of remote sensing on water body mirror reflection[J].Remote Sensing of Environment,1994,9(3):195-202.[季耿善,杨静,傅江.水体遥感的镜面反射特性研究[J].环境遥感,1994,9(3):195-202.]
[24] Mohan M, Prakash Chauhan. Simulations for optimal payload tilt to avoid sunglint in IRS-P4 Ocean Colour Monitor (OCM) data around the Indian subcontinent[J].International Journal of Remote Sensing,2001, 22, (1):185-190.
[25] Fargion G S, Mueller J L. Ocean Optics Protocols for SeaWiFS Validation[R]. Revision 2. NASA/TM-2000-209966, 2000.
[26] Gordon H R, Kenneth J Voss. MODIS Normalized Water leaving radiance Algorithm Theoretical Basis Document (mod 18)\[R\].Version 4, 1999.
[27] Morel A, Gentili B. Practical application of the “turbid water” flag in ocean color imagery: Interference with sun-glint contaminated pixels in open ocean[J].Remote Sensing of Environment,2008,112:934-938.
[28] Mobley C D. Estimation of the Remote-Sensing Reflectance from abovesurface measurements[J].Applied Optics, 1999, 38:7 442-7 455.
[29] Lee Z P, Carder K L, Peacock T G, et al. Remote sensing reflectance measured with and without a vertical polarizer[J].SPIE, 1997, 2963: 483-488.
[30] Wang Xiaoyong, Li Tongji, Tang Junwu,et al. Measurement and analysis of AOPs in case II waters with above-water method[J].Ocean Technology, 2004,23(2):1-6.[汪小勇,李铜基,唐军武,等.二类水体表观光学特性的测量与分析—水面之上法方法研究[J].海洋技术, 2004,23(2):1-6.] 
[31] Fougnie B, Frouin R, Lecomte P, et al. Reduction of skylight reflection effects in the above-water measurement of diffuse marine reflectance[J]. Applied Optics,1999, 38(18):3 844-3 856.
[32] Zhou Guanhua, Liu Zhigang, Liu Qinhuo, et al. Polarization information of ocean color remote sensing[J]. Journal of Remote Sensing, 2008,12(2):159-167.[周冠华,刘志刚,柳钦火,等.水色遥感中偏振信息的研究进展[J].遥感学报,2008,12(2):159-167.]
[33] Qi Feng, Wang Xuejun. Application of remote sensing techniques in monitoring and assessing inland water quality[J].Advances in Environmental Science,1999,7(3):90-99.[齐锋,王学军.内陆水体水质监测与评价中的遥感应用[J]. 环境科学进展,1999,7(3):90-99.]
[34] Tang Junwu, Wang Xiaomei, Song Qingjun, et al. Statistical inversion models for case II water color elements in the Yellow sea and east China sea[J]. Advances in Marine Science,2004, 22(B10):1-7.[唐军武,王晓梅,宋庆君,等.黄、东海二类水体水色要素的统计反演模式[J]. 海洋科学进展, 2004, 22(B10):1-7.]
[35] Xing Xiaogang, Zhao Dongzhi, Liu Yuguang, et al. Progress in fluorescence remote sensing of chlorophyll-a[J].Journal of Remote Sensing,2007,11(1):137-144. [邢小罡,赵冬至,刘玉光,等.叶绿素a荧光遥感研究进展[J]. 遥感学报,2007,11(1):137-144.]
[36] Kutser T, Herlevi A, Kallio K, et al. A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes[J].The Science of the Total Environment,2001, 268: 47-58.
[37] Morel A, B langer S. Improved detection of turbid waters from ocean color sensors information[J].Remote Sensing of Environment,2006,102:237-249.[38] Vahtmae E, Kutser E, Martin G, et al. Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters-a Baltic sea case study[J].Remote Sensing of Environment,2006,101:342-351.
[39] Albert A, Mobley C D. An analytical model from subsurface irradiance and remote sensing reflectance in deep and shallow case-waters[J].Optics Express,2005, 11(22): 2 873-2 879.
[40] Doerffer R, Fischer J. Concentrations of chlorophyll, suspended matter, and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods[J].Journal of Geophysical Research,1994, 99:7 457-7 466.
[41] Zhan Haigang. Iinversion of ocean color remote sensing based on soft computing[D]. Qingdao: Ocean University of Qingdao,2001.[詹海刚. 基于软计算的海洋水色遥感反演[D]. 青岛:中国青岛海洋大学, 2001.]
[42] IOCCG. Remote sensing of inherent optical properties: Fundamentals, tests of algorithms, and applications[C]// Lee Z P, ed. Reports of the International Ocean-Colour Coordinating Group, No.5, IOCCG, Dartmouth, Canada,2006.
[43] Pan Delu, Ma Ronghua. Several key problems of lake water quality remote sensing[J].Journal of Lake Sciences, 2008,20(2):139-144.[潘德炉,马荣华.湖泊水质遥感几个关键问题[J].湖泊科学,2008,20(2):139-144.]

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