基于GOCI影像的湖泊悬浮物浓度分类反演
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摘要
悬浮物直接影响到光在水体中的传播,进而影响着水生生态环境,最终决定了湖泊的初级生产力。传统的遥感反演估算模型大多是针对某一湖区进行统一建模,忽视了不同区域水体光学性质的复杂差异性,并且传统的传感器时间分辨率和空间分辨率受到一定限制。针对太湖、巢湖、滇池、洞庭湖4个湖区利用两步聚类法将高光谱模拟到GOCI影像上的波段进行分类,将水体类型分为三类,第一类水体为悬浮物主导的水体,第二类水体为悬浮物和叶绿素a共同主导的水体,第三类水体为叶绿素a主导的水体。针对不同类型水体的光学特征,分别构建了悬浮物浓度反演模型,结果表明第一类水体可以利用B7/B4,第二和第三类水体可以利用B7/(B8+B4)作为波段组合因子对悬浮物浓度进行模型构建。精度验证结果表明,分类建模后第一类和第三类水体悬浮物浓度估算精度都得到了较明显提高,第一类水体RMSE降低了9.19mg/L,MAPE降低了3%,第三类水体RMSE降低了5.63 mg/L,MAPE降低了13.97%,第二类水体精度稍有降低。最后将反演模型应用于2013年5月13日的GOCI影像,可知整体而言太湖西南部地区悬浮物浓度较高,东北部地区悬浮物浓度较低,并且从9:00到15:00,太湖南部悬浮物浓度较高的区域在逐渐缩小。
Total suspended matter( TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment,and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built for specific study areas,ignoring variation in the optical properties of water among diverse areas. In addition,common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake,Chaohu Lake,Dianchi Lake,and Dongting Lake were selected as our study lakes,and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager( GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM,the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a( Chl-a),and the optical properties of Water Type 3 weremainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7 / B4 for Water Type 1 and B7 /( B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors( RMSEs) and minimum absolute percentage errors( MAPEs) of Water Type 1 were 9. 19 mg / L and3%,and those of Water Type 3 were 5. 63 mg / L and 13. 97%,respectively,which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13,2013 using this classification method,and the results showed that the concentration of TSM was higher in the southwest than in the northeast. In addition,the area of higher TSM concentration in the southern region of the lake was reduced from 9: 00 to 15: 00( Beijing Local Time).
Total suspended matter( TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment,and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built for specific study areas,ignoring variation in the optical properties of water among diverse areas. In addition,common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake,Chaohu Lake,Dianchi Lake,and Dongting Lake were selected as our study lakes,and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager( GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM,the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a( Chl-a),and the optical properties of Water Type 3 weremainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7 / B4 for Water Type 1 and B7 /( B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors( RMSEs) and minimum absolute percentage errors( MAPEs) of Water Type 1 were 9. 19 mg / L and3%,and those of Water Type 3 were 5. 63 mg / L and 13. 97%,respectively,which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13,2013 using this classification method,and the results showed that the concentration of TSM was higher in the southwest than in the northeast. In addition,the area of higher TSM concentration in the southern region of the lake was reduced from 9: 00 to 15: 00( Beijing Local Time).
引文
[1]光洁,韦玉春,黄家柱,李云梅,闻建光,郭建平.分季节的太湖悬浮物遥感估测模型研究.湖泊科学,2007,19(3):241-249.
[2]陈晓翔,丁晓英.用FY-1D数据估算珠江口海域悬浮泥沙含量.中山大学学报:自然科学版,2004,43(S1):194-196.
[3]江文胜,苏健,杨华,张英娟,姜华,王庆业,张凯,田恬.渤海悬浮物浓度分布和水动力特征的关系.海洋学报,2002,24(S1):212-217.
[4]马荣华,戴锦芳.结合Landsat ETM与实测光谱估测太湖叶绿素及悬浮物含量.湖泊科学,2005,17(2):97-103.
[5]Binding C E,Bowers D G,Mitchelson-Jacob E G.An algorithm for the retrieval of suspended sediment concentrations in the Irish Sea from Sea Wi FS ocean colour satellite imagery.International Journal of Remote Sensing,2003,24(19):3791-3806.
[6]吕恒,魏小鸿.太湖悬浮物浓度的MODIS数据定量反演提取.地球信息科学学报,2008,10(2):151-155.
[7]Eleveld M A,van der Wal D,van Kessel T.Estuarine suspended particulate matter concentrations from sun-synchronous satellite remote sensing:tidal and meteorological effects and biases.Remote Sensing of Environment,2014,143:204-215.
[8]Xi H Y,Zhang Y Z.Total suspended matter observation in the Pearl River estuary from in situ and MERIS data.Environmental Monitoring and Assessment,2011,177(1/4):563-574.
[9]Kaba E,Philpot W,Steenhuis T.Evaluating suitability of MODIS-Terra images for reproducing historic sediment concentrations in water bodies:Lake Tana,Ethiopia.International Journal of Applied Earth Observation and Geoinformation,2014,26:286-297.
[10]Ryu J H,Han H J,Cho S,Park Y J,Ahn Y H.Overview of geostationary ocean color imager(GOCI)and GOCI data processing system(GDPS).Ocean Science Journal,2012,47(3):223-233.
[11]Choi J K,Park Y J,Lee B R,Eom J,Moon J E,Ryu J H.Application of the Geostationary Ocean Color Imager(GOCI)to mapping the temporal dynamics of coastal water turbidity.Remote Sensing of Environment,2014,146:24-35.
[12]He X Q,Bai Y,Pan D L,Huang N L,Dong X,Chen J S,Chen C T A,Cui Q F.Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters.Remote Sensing of Environment,2013,133:225-239.
[13]Doxaran D,Lamquin N,Park Y J,Mazeran C,Ryu J H,Wang M H,Poteau A.Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS,MERIS and GOCI satellite data.Remote Sensing of Environment,2014,146:36-48.
[14]Vanhellemont Q,Neukermans G,Ruddick K.Synergy between polar-orbiting and geostationary sensors:remote sensing of the ocean at high spatial and high temporal resolution.Remote Sensing of Environment,2014,146(5):49-62.
[15]Ruddick K,Vanhellemont Q,Yan J,Neukermans G,Wei G M,Shang S L.Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager(GOCI).Ocean Science Journal,2012,47(3):331-345.
[16]Arst H,Reinart A.Application of optical classifications to North European lakes.Aquatic Ecology,2009,43(4):789-801.
[17]刘忠华.基于高分数据的太湖重点污染入湖河流叶绿素a浓度遥感反演[D].南京:南京师范大学,2012:1-77.
[18]周晓宇,孙德勇,李云梅,李俊生,龚绍琦.结合水体光学分类反演太湖总悬浮物浓度.环境科学,2013,34(7):2618-2627.
[19]Shi K,Li Y M,Li L,Lu H,Song K S,Liu Z H,Xu Y F,Li Z C.Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.Science of the Total Environment,2013,444:1-15.
[20]安斌,陈书海,严卫东.SAM法在多光谱图像分类中的应用.中国体视学与图像分析,2005,10(1):55-60.
[21]Sun D Y,Li Y M,Wang Q,Le C F,Huang C C,Shi K.Development of optical criteria to discriminate various types of highly turbid lake waters.Hydrobiologia,2011,669(1):83-104.
[22]查桂红.基于GOCI影像的内陆水体悬浮物浓度遥感估算研究[D].南京:南京师范大学,2013:1-75.
[23]Zhang Y L,Shi K,Liu X H,Zhou Y Q,Qin B Q.Lake topography and wind waves determining seasonal-spatial dynamics of total suspended matter in turbid Lake Taihu,China:assessment using long-term high-resolution MERIS data.PLo S ONE,2014,9(5):e98055.
[24]李渊,李云梅,施坤,吕恒,郭宇龙,周莉,刘阁.基于光谱分类的总悬浮物浓度估算.光谱学与光谱分析,2013,33(10):2721-2726.
[25]Ma W,Xing Q,Chen C,Zhang Y,Yu D,Shi P.Using the normalized peak area of remote sensing reflectance in the near-infrared region to estimate total suspended matter.International Journal of Remote Sensing,2011,32(22):7479-7486.
[26]施坤,李云梅,刘忠华,徐祎凡,徐昕,马万泉,陆超平.基于半分析方法的内陆湖泊水体总悬浮物浓度遥感估算研究.环境科学,2011,32(6):1571-1580.
[27]Zhang B,Li J,Shen Q,Chen D.A bio-optical model based method of estimating total suspended matter of Lake Taihu from near-infrared remote sensing reflectance.Environmental Monitoring and Assessment,2008,145(1/3),339-347.
[28]盖利亚,张继贤,刘正军.基于光谱特征的三峡坝区水色要素反演.测绘科学,2008,33(1):96-99.
[29]宋庆君,马荣华,唐军武,王晓梅.秋季太湖悬浮物高光谱估算模型.湖泊科学,2008,20(2):196-20.
[2]陈晓翔,丁晓英.用FY-1D数据估算珠江口海域悬浮泥沙含量.中山大学学报:自然科学版,2004,43(S1):194-196.
[3]江文胜,苏健,杨华,张英娟,姜华,王庆业,张凯,田恬.渤海悬浮物浓度分布和水动力特征的关系.海洋学报,2002,24(S1):212-217.
[4]马荣华,戴锦芳.结合Landsat ETM与实测光谱估测太湖叶绿素及悬浮物含量.湖泊科学,2005,17(2):97-103.
[5]Binding C E,Bowers D G,Mitchelson-Jacob E G.An algorithm for the retrieval of suspended sediment concentrations in the Irish Sea from Sea Wi FS ocean colour satellite imagery.International Journal of Remote Sensing,2003,24(19):3791-3806.
[6]吕恒,魏小鸿.太湖悬浮物浓度的MODIS数据定量反演提取.地球信息科学学报,2008,10(2):151-155.
[7]Eleveld M A,van der Wal D,van Kessel T.Estuarine suspended particulate matter concentrations from sun-synchronous satellite remote sensing:tidal and meteorological effects and biases.Remote Sensing of Environment,2014,143:204-215.
[8]Xi H Y,Zhang Y Z.Total suspended matter observation in the Pearl River estuary from in situ and MERIS data.Environmental Monitoring and Assessment,2011,177(1/4):563-574.
[9]Kaba E,Philpot W,Steenhuis T.Evaluating suitability of MODIS-Terra images for reproducing historic sediment concentrations in water bodies:Lake Tana,Ethiopia.International Journal of Applied Earth Observation and Geoinformation,2014,26:286-297.
[10]Ryu J H,Han H J,Cho S,Park Y J,Ahn Y H.Overview of geostationary ocean color imager(GOCI)and GOCI data processing system(GDPS).Ocean Science Journal,2012,47(3):223-233.
[11]Choi J K,Park Y J,Lee B R,Eom J,Moon J E,Ryu J H.Application of the Geostationary Ocean Color Imager(GOCI)to mapping the temporal dynamics of coastal water turbidity.Remote Sensing of Environment,2014,146:24-35.
[12]He X Q,Bai Y,Pan D L,Huang N L,Dong X,Chen J S,Chen C T A,Cui Q F.Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters.Remote Sensing of Environment,2013,133:225-239.
[13]Doxaran D,Lamquin N,Park Y J,Mazeran C,Ryu J H,Wang M H,Poteau A.Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS,MERIS and GOCI satellite data.Remote Sensing of Environment,2014,146:36-48.
[14]Vanhellemont Q,Neukermans G,Ruddick K.Synergy between polar-orbiting and geostationary sensors:remote sensing of the ocean at high spatial and high temporal resolution.Remote Sensing of Environment,2014,146(5):49-62.
[15]Ruddick K,Vanhellemont Q,Yan J,Neukermans G,Wei G M,Shang S L.Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager(GOCI).Ocean Science Journal,2012,47(3):331-345.
[16]Arst H,Reinart A.Application of optical classifications to North European lakes.Aquatic Ecology,2009,43(4):789-801.
[17]刘忠华.基于高分数据的太湖重点污染入湖河流叶绿素a浓度遥感反演[D].南京:南京师范大学,2012:1-77.
[18]周晓宇,孙德勇,李云梅,李俊生,龚绍琦.结合水体光学分类反演太湖总悬浮物浓度.环境科学,2013,34(7):2618-2627.
[19]Shi K,Li Y M,Li L,Lu H,Song K S,Liu Z H,Xu Y F,Li Z C.Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.Science of the Total Environment,2013,444:1-15.
[20]安斌,陈书海,严卫东.SAM法在多光谱图像分类中的应用.中国体视学与图像分析,2005,10(1):55-60.
[21]Sun D Y,Li Y M,Wang Q,Le C F,Huang C C,Shi K.Development of optical criteria to discriminate various types of highly turbid lake waters.Hydrobiologia,2011,669(1):83-104.
[22]查桂红.基于GOCI影像的内陆水体悬浮物浓度遥感估算研究[D].南京:南京师范大学,2013:1-75.
[23]Zhang Y L,Shi K,Liu X H,Zhou Y Q,Qin B Q.Lake topography and wind waves determining seasonal-spatial dynamics of total suspended matter in turbid Lake Taihu,China:assessment using long-term high-resolution MERIS data.PLo S ONE,2014,9(5):e98055.
[24]李渊,李云梅,施坤,吕恒,郭宇龙,周莉,刘阁.基于光谱分类的总悬浮物浓度估算.光谱学与光谱分析,2013,33(10):2721-2726.
[25]Ma W,Xing Q,Chen C,Zhang Y,Yu D,Shi P.Using the normalized peak area of remote sensing reflectance in the near-infrared region to estimate total suspended matter.International Journal of Remote Sensing,2011,32(22):7479-7486.
[26]施坤,李云梅,刘忠华,徐祎凡,徐昕,马万泉,陆超平.基于半分析方法的内陆湖泊水体总悬浮物浓度遥感估算研究.环境科学,2011,32(6):1571-1580.
[27]Zhang B,Li J,Shen Q,Chen D.A bio-optical model based method of estimating total suspended matter of Lake Taihu from near-infrared remote sensing reflectance.Environmental Monitoring and Assessment,2008,145(1/3),339-347.
[28]盖利亚,张继贤,刘正军.基于光谱特征的三峡坝区水色要素反演.测绘科学,2008,33(1):96-99.
[29]宋庆君,马荣华,唐军武,王晓梅.秋季太湖悬浮物高光谱估算模型.湖泊科学,2008,20(2):196-20.