半封闭海湾Chl a浓度遥感反演模型比较性研究——以胶州湾为例
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摘要
叶绿素是我国近海水质监测的主要参数之一,其浓度的遥感反演是监测水体光学特性、评价水体污染的重要指标。本研究以Landsat-8/OLI、FY-3A/MERSI和HJ-1B/CCD遥感影像为数据源,结合2016年实测的Chl a浓度和水体光谱特征,建立胶州湾Chl a浓度的半经验/半分析反演模型。研究表明:基于Landsat-8建立的反演模型,整体Pearson相关系数最高,最优模型的预测值与实测值的决定系数R~2>0.86,反演效果最好,能较好的适应于胶州湾Chl a浓度的反演研究。Landsat 8最佳波段组合为:2月份Band4/Band2,R~2=0.83;5月份[(Band3)~(-1)-(Band4)~(-1)]*Band5,R~2=0.80;8月份[(Band2)~(-1)-(Band3)~(-1)]*Band4,R~2=0.78;11月份[(Band4)~(-1)-(Band2)~(-1)]*Band1,R~2=0.86。
Chlorophyll is one of the main parameters of water quality monitoring in coastal waters of China.The remote sensing inversion of its concentration is an important index to monitor the water optical characteristics and evaluate water pollution.In this study,quasi-synchronous Landsat-8/OLI,FY-3A/MERSI and HJ-1B/CCD remote sensing images are used,and combined with the in-situ chlorophyll-a concentration and water spectral characteristics in 2016,a semiempirical and semi-analytical inversion model of chlorophyll-a concentration in Jiaozhou bay is established.The research shows:the inversion model with the highest correlation coefficient is derived from Landsat-8,the determinant coefficient,R~2,between the in-situ data and predicted values of the optimal model is>0.86.Furthermore,the inversion effect is satisfied,and the results can be well adapted to the inversion of chlorophyll-a concentration in Jiaozhou bay.The optimum band combination of landsat 8 is:February Band4/Band2,R~2=0.83;May[(Band3)~(-1)-(Band4)~(-1)]*Band5,R~2=0.80;August[(Band2)~(-1)-(Band3)~(-1)]*Band4,R~2=0.78;November[(Band4)~(-1)-(Band2)~(-1)]*Band1,R~2=0.86.
Chlorophyll is one of the main parameters of water quality monitoring in coastal waters of China.The remote sensing inversion of its concentration is an important index to monitor the water optical characteristics and evaluate water pollution.In this study,quasi-synchronous Landsat-8/OLI,FY-3A/MERSI and HJ-1B/CCD remote sensing images are used,and combined with the in-situ chlorophyll-a concentration and water spectral characteristics in 2016,a semiempirical and semi-analytical inversion model of chlorophyll-a concentration in Jiaozhou bay is established.The research shows:the inversion model with the highest correlation coefficient is derived from Landsat-8,the determinant coefficient,R~2,between the in-situ data and predicted values of the optimal model is>0.86.Furthermore,the inversion effect is satisfied,and the results can be well adapted to the inversion of chlorophyll-a concentration in Jiaozhou bay.The optimum band combination of landsat 8 is:February Band4/Band2,R~2=0.83;May[(Band3)~(-1)-(Band4)~(-1)]*Band5,R~2=0.80;August[(Band2)~(-1)-(Band3)~(-1)]*Band4,R~2=0.78;November[(Band4)~(-1)-(Band2)~(-1)]*Band1,R~2=0.86.
引文
[1]AYRES W S,BUSIA A,DINAR A,et al.Integrated lake and reservoir management[R].Washington:The World Bank,1996.
[2]董金海,焦念志.胶州湾生态学研究[M].北京:科学出版社,1995:47-51.
[3]YANG D F,GAO Z H,CHEN Y,et al. Examination of silicate limitation of primary production in Jiaozhou Bay,North China[J]. Chinese Journal of Oceanology and Limnology,2003,21(2):114-133.
[4]WU Z S,HE H,CAI Y J,et al.Spatial distribution of chlorophyll a and its relationship with the enviro nment during summer in Lake Poyang:a Yangtze-connected lake[J].Hydrobiologia,2014,732(1):61-70.
[5]朱利,李云梅,赵少华,等.基于GF-1号卫星WFV数据的太湖水质遥感监测[J].国土资源遥感,2015,27(1):113-120.
[6]HARMEL T,CHAMI M.Estimation of the sunglint radiance field from optical satellite imagery over open ocean:multidirectional approach and polarization aspects[J].Journal of Geophysical Research:Oceans,2013,118(1):76-90.
[7] PRIEUR L,SATHYENDRANATH S.An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments,dissolved organic matter,and other particulate materials[J]. Limnology and Oceanography,1981,26(4):671-689.
[8] SCHALLES J F,HLADIK C M. Mapping phytoplankton chlorophyll in turbid,Case 2 estuarine and coastal waters[J].Israel Journal of Plant Sciences,2012,60(1/2):169-191.
[9] KOPONEN S,PULLIAINEN J,KALLIO K,et al. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data[J]. Remote Sensing of Environment,2002,79(1):51-59.
[10] LEVIN N,HEIMOWITZ A. Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS[J]. Remote Sensing of Environment,2012,126:12-26.
[11]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[J].湖泊科学,2005,17(2):97-103.
[12] SONGK S,LI L,TEDESCO L P,et al. Remote estimation of chlorophyll-a in turbid inland waters:three-band model versus GA-PLS model[J]. Remote Sensing of Environment,2013,136:342-357.
[13]GIORGIO D O,ANATOIY A.Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:experimental results[J]. Applied Optics,2005,44(3):412-422.
[14]GITELSON A A,DALL'OLMO G,MOSES W,et al. A simple semi-analytical model for remote estimation of chlorophyll\a in turbid waters:validation[J]. Remote Sensing of Environment,2008,112(9):3582-3593.
[15]MARTINEZ-VICENTE V,DALL'OLMO G,TARRAN G,et al.Optical backscattering is correlated with phytoplankton carbon across the Atlantic Ocean[J]. Geophysical Research Letters,2013,40(6):1154-1158.
[16]NOVOA S,CHUST G,VALENCIA V,et al.Estimation of chlorophyll-a concentration in waters over the continental shelf of the Bay of Biscay:a comparison of remote sensing algorithms[J].International Journal of Remote Sensing,2011,32(23):8349-8371.
[17]周冠华,柳钦火,马荣华,等.基于半分析模型的波段最优化组合反演混浊太湖水体Chl a[J].湖泊科学,2008,20(2):153-159.
[18]郝艳玲,曹文熙,崔廷伟,等.基于半分析算法的赤潮水体固有光学性质反演[J].海洋学报,2011,33(1):52-65.
[19]韩秀珍,郑伟,刘诚,等.基于MERSI和MODIS的太湖水体Chl a含量反演[J].地理研究,2011,30(2):291-300.
[20]张鸽,黄方,龚绍琦,等.基于FY-3A/MERSI数据和支持向量机的太湖水体悬浮物浓度遥感模型[J].东北师大学报:自然科学版,2016,48(1):148-153.
[21] DALL’OLMO G,GITELSON A A. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:Experimental results[J].Applied Optics,2005,44(3):412-422.
[22]WATANABE F S Y,ALCNTARA E,RODRIGUES T W P,et al. Estimation of Chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using OLI/Landsat-8 images[J]. International Journal of Environmental Research and Public Health,2015,12(9):10391-10417.
[23]JAELANI L M,LIMEHUWEY R,KURNIADIN N,et al.Estimation of TSS and Chl-a concentration from Landsat 8-OLI:the effect of atmosphere and retrieval algorithm[J]. IPTEK the Journal for Technology and Science,2016,27(1):16-23.
[24]黄灵光,方豫,张大文,等.基于Landsat-8 OLI的鄱阳湖Chl a浓度定量反演[J].江西科学,2016,34(4):441-444,456.
[2]董金海,焦念志.胶州湾生态学研究[M].北京:科学出版社,1995:47-51.
[3]YANG D F,GAO Z H,CHEN Y,et al. Examination of silicate limitation of primary production in Jiaozhou Bay,North China[J]. Chinese Journal of Oceanology and Limnology,2003,21(2):114-133.
[4]WU Z S,HE H,CAI Y J,et al.Spatial distribution of chlorophyll a and its relationship with the enviro nment during summer in Lake Poyang:a Yangtze-connected lake[J].Hydrobiologia,2014,732(1):61-70.
[5]朱利,李云梅,赵少华,等.基于GF-1号卫星WFV数据的太湖水质遥感监测[J].国土资源遥感,2015,27(1):113-120.
[6]HARMEL T,CHAMI M.Estimation of the sunglint radiance field from optical satellite imagery over open ocean:multidirectional approach and polarization aspects[J].Journal of Geophysical Research:Oceans,2013,118(1):76-90.
[7] PRIEUR L,SATHYENDRANATH S.An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments,dissolved organic matter,and other particulate materials[J]. Limnology and Oceanography,1981,26(4):671-689.
[8] SCHALLES J F,HLADIK C M. Mapping phytoplankton chlorophyll in turbid,Case 2 estuarine and coastal waters[J].Israel Journal of Plant Sciences,2012,60(1/2):169-191.
[9] KOPONEN S,PULLIAINEN J,KALLIO K,et al. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data[J]. Remote Sensing of Environment,2002,79(1):51-59.
[10] LEVIN N,HEIMOWITZ A. Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS[J]. Remote Sensing of Environment,2012,126:12-26.
[11]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[J].湖泊科学,2005,17(2):97-103.
[12] SONGK S,LI L,TEDESCO L P,et al. Remote estimation of chlorophyll-a in turbid inland waters:three-band model versus GA-PLS model[J]. Remote Sensing of Environment,2013,136:342-357.
[13]GIORGIO D O,ANATOIY A.Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:experimental results[J]. Applied Optics,2005,44(3):412-422.
[14]GITELSON A A,DALL'OLMO G,MOSES W,et al. A simple semi-analytical model for remote estimation of chlorophyll\a in turbid waters:validation[J]. Remote Sensing of Environment,2008,112(9):3582-3593.
[15]MARTINEZ-VICENTE V,DALL'OLMO G,TARRAN G,et al.Optical backscattering is correlated with phytoplankton carbon across the Atlantic Ocean[J]. Geophysical Research Letters,2013,40(6):1154-1158.
[16]NOVOA S,CHUST G,VALENCIA V,et al.Estimation of chlorophyll-a concentration in waters over the continental shelf of the Bay of Biscay:a comparison of remote sensing algorithms[J].International Journal of Remote Sensing,2011,32(23):8349-8371.
[17]周冠华,柳钦火,马荣华,等.基于半分析模型的波段最优化组合反演混浊太湖水体Chl a[J].湖泊科学,2008,20(2):153-159.
[18]郝艳玲,曹文熙,崔廷伟,等.基于半分析算法的赤潮水体固有光学性质反演[J].海洋学报,2011,33(1):52-65.
[19]韩秀珍,郑伟,刘诚,等.基于MERSI和MODIS的太湖水体Chl a含量反演[J].地理研究,2011,30(2):291-300.
[20]张鸽,黄方,龚绍琦,等.基于FY-3A/MERSI数据和支持向量机的太湖水体悬浮物浓度遥感模型[J].东北师大学报:自然科学版,2016,48(1):148-153.
[21] DALL’OLMO G,GITELSON A A. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:Experimental results[J].Applied Optics,2005,44(3):412-422.
[22]WATANABE F S Y,ALCNTARA E,RODRIGUES T W P,et al. Estimation of Chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using OLI/Landsat-8 images[J]. International Journal of Environmental Research and Public Health,2015,12(9):10391-10417.
[23]JAELANI L M,LIMEHUWEY R,KURNIADIN N,et al.Estimation of TSS and Chl-a concentration from Landsat 8-OLI:the effect of atmosphere and retrieval algorithm[J]. IPTEK the Journal for Technology and Science,2016,27(1):16-23.
[24]黄灵光,方豫,张大文,等.基于Landsat-8 OLI的鄱阳湖Chl a浓度定量反演[J].江西科学,2016,34(4):441-444,456.
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