山东典型污灌区冬小麦叶片重金属高光谱反演及空间分布特征
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摘要
为了大面积、实时监测污水灌溉区冬小麦重金属胁迫状况,以冬小麦叶片重金属Cr、Ni、Pb、Zn、Hg、Cd为研究对象,利用冬小麦冠层光谱数据及金属含量数据,采用逐步多元线性回归(SMLR)和偏最小二乘回归(PLSR)的方法,建立基于原始光谱反射率(R)、反射率一阶微分(FDR)、反射率二阶微分(SDR)、光谱参数(SP)的8种冠层光谱反演模型,通过分析所建模型精度,选取最优反演模型,实现研究区内冬小麦叶片重金属的定量反演。结果表明,对于Pb、Zn、Cd,基于反射率一阶微分的偏最小二乘回归模型(FDR-PLSR)为最优模型[Pb:决定系数(R~2)=0.848,相对分析误差(RPD)=1.598;Zn:R~2=0.790,RPD=2.295;Cd:R~2=0.868,RPD=2.406];对于Cr,基于反射率二阶微分的偏最小二乘回归模型(SDR-PLSR)为最优模型(R~2=0.846,RPD=2.013);对于Ni、Hg,基于光谱参数的偏最小二乘回归模型(SP-PLSR)为最优模型(Ni:R~2=0.887,RPD=1.872;Hg:R~2=0.819,RPD=1.684)。从空间插值结果可以看出,冬小麦叶片中Cr、Ni含量在研究区东南部较高,北部及西北部较低;Pb、Zn含量在中部以及南部较高;Hg含量在西北部较低;Cd含量在中部、北部、西北部较低。
In order to monitor the heavy metal stress of winter wheat in sewage irrigation area in large area and in real time,heavy metals Cr,Ni,Pb,Zn,Hg and Cd in winter wheat were selected as the research objects,the stepwise multiple linear regression( SMLR) and partial least squares regression( PLSR) methods were used to establish the 8 kinds of retrieval models based on canopy spectra of raw spectra reflectance( R),first derivate reflectance( FDR),second derivate reflectance( SDR) and spectral parameters( SP) using the data of winter wheat canopy spectrum and heavy metal contents. The optimal retrieval model was selected by analyzing the accuracy of the model,and the quantitative retrieval of heavy metals in winter wheat was studied. The results showed that the FDR-PLSR model was the optimal model for Pb,the value of R~2 was 0. 848,the value of RPD was 1. 598; the FDR-PLSR model was the optimal model for Zn,the value of R~2 was 0. 790,the value of RPD was 2. 295; the FDR-PLSR model was the optimal model for Cd,the value of R~2 was 0. 868,the value of RPD was 2. 406; the SDR-PLSR model wasthe optimal model for Cr,the value of R~2 was 0. 846,the value of RPD was 2. 013; the SP-PLSR model was the optimal model for Ni,the value of R~2 was 0. 887,the value of RPD was 1. 872; the SP-PLSR model was the optimal model for Hg,the value of R~2 was 0. 819,the value of RPD was 1. 684. The optimum model of soil heavy metal was used to interpolate the heavy metal content,the high value areas of Cr and Ni contents in winter wheat leaves were found in the southeast of the study area,and the contents of Pb and Zn were relatively low in the north and northwest of the study area,while the contents of Pb and Zn were higher in the middle and south,the content of Hg in the northwest was lower than that in other places,and the Cd content was low in the middle,north and northwest of the study area.
In order to monitor the heavy metal stress of winter wheat in sewage irrigation area in large area and in real time,heavy metals Cr,Ni,Pb,Zn,Hg and Cd in winter wheat were selected as the research objects,the stepwise multiple linear regression( SMLR) and partial least squares regression( PLSR) methods were used to establish the 8 kinds of retrieval models based on canopy spectra of raw spectra reflectance( R),first derivate reflectance( FDR),second derivate reflectance( SDR) and spectral parameters( SP) using the data of winter wheat canopy spectrum and heavy metal contents. The optimal retrieval model was selected by analyzing the accuracy of the model,and the quantitative retrieval of heavy metals in winter wheat was studied. The results showed that the FDR-PLSR model was the optimal model for Pb,the value of R~2 was 0. 848,the value of RPD was 1. 598; the FDR-PLSR model was the optimal model for Zn,the value of R~2 was 0. 790,the value of RPD was 2. 295; the FDR-PLSR model was the optimal model for Cd,the value of R~2 was 0. 868,the value of RPD was 2. 406; the SDR-PLSR model wasthe optimal model for Cr,the value of R~2 was 0. 846,the value of RPD was 2. 013; the SP-PLSR model was the optimal model for Ni,the value of R~2 was 0. 887,the value of RPD was 1. 872; the SP-PLSR model was the optimal model for Hg,the value of R~2 was 0. 819,the value of RPD was 1. 684. The optimum model of soil heavy metal was used to interpolate the heavy metal content,the high value areas of Cr and Ni contents in winter wheat leaves were found in the southeast of the study area,and the contents of Pb and Zn were relatively low in the north and northwest of the study area,while the contents of Pb and Zn were higher in the middle and south,the content of Hg in the northwest was lower than that in other places,and the Cd content was low in the middle,north and northwest of the study area.
引文
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[21]Ren H Y,Zhuang D F,Yang J X,et al.A feasibility study of NIR spectra in identifying heavy metal contamination in rice around abandoned tailing ponds:A case study in Guiyang county in south China[J].Journal of Geophysics&Remote Sensing,2013,2(2):204.
[22]吉曹翔,李崇,关艳玲,等.基于小波变换的水稻砷污染光谱奇异性研究[J].现代农业科技,2015(3):199-200.
[23]迟光宇,刘新会,刘素红,等.Cu污染与小麦特征光谱相关关系研究[J].光谱学与光谱分析,2006,22(7):62-66.
[24]吴传庆,童庆禧,郑兰芬.地面、图像光谱数据的预处理[J].遥感技术与应用,2005,20(5):506-511.
[25]林海军,张绘芳,高亚琪,等.基于马氏距离法的荒漠树种高光谱识别[J].光谱学与光谱分析,2014,34(12):3358-3362.
[26]邓书斌,陈秋锦,杜会建,等.ENVI遥感图像处理方法[M].2版.北京:高等教育出版社,2014:381-383.
[27]彭杰,迟春明,向红英,等.基于连续统去除法的土壤盐分含量反演研究[J].土壤学报,2014,67(3):459-469.
[28]彭杰,王家强,向红英,等.土壤含盐量与电导率的高光谱反演精度对比研究[J].光谱学与光谱分析,2014,34(2):510-514.
[29]李丙智,李敏夏,周璇,等.苹果树叶片全氮含量高光谱估算模型研究[J].遥感学报,2010,14(4):761-773.
[30]王菲,王集宁,曹文涛,等.山东省焦家式金矿区土壤重金属铬高光谱监测研究[J].光谱学与光谱分析,2017,37(5):1649-1655.
[31]Zhong P,Zhang P,Wang R.Dynamic learning of SMLR for feature selection and classification of hyperspectral data[J].IEEE Geoscience&Remote Sensing Letters,2008,5(2):280-284.
[32]杨可明,汪国平,尤笛,等.重金属铅离子胁迫下玉米叶片光谱弱差信息的DSAT甄别模型[J].光谱学与光谱分析,2016,36(8):2568-2572.
[33]陈思宁,刘新会,侯娟,等.重金属锌胁迫的白菜叶片光谱响应研究[J].光谱学与光谱分析,2007,27(9):1797-1801.
[2]李政红,张胜,马琳娜,等.污灌区土壤重金属污染分布及其影响因素研究[J].干旱区资源与环境,2010,24(11):166-169.
[3]Kim H K,Jang T I,Kim S M,et al.Impact of domestic wastewater irrigation on heavy metal contamination in soil and vegetables[J].Environmental Earth Sciences,2015,73(5):2377-2383.
[4]杜瑜,尚琪.环境镉污染人群健康影响研究回顾[J].卫生研究,2006,35(2):241-243.
[5]李霞,张慧鸣,徐震,等.农田Cd和Hg污染的来源解析与风险评价研究[J].农业环境科学学报,2016,35(7):1314-1320.
[6]金姝兰,黄益宗,王斐,等.江西铜矿及冶炼厂周边土壤和农作物稀土元素含量与评价[J].环境科学,2015,36(3):1060-1068.
[7]施翔,陈益泰,王树凤,等.废弃尾矿库15种植物对重金属Pb、Zn的积累和养分吸收[J].环境科学,2012,33(6):2021-2027.
[8]梁亮,张连蓬,林卉,等.基于导数光谱的小麦冠层叶片含水量反演[J].中国农业科学,2013,46(1):18-29.
[9]王慧,曾路生,孙永红,等.重金属铜和锌胁迫下的小麦冠层反射光谱特征[J].农业工程学报,2017,33(2):171-176.
[10]任红艳,庄大方,潘剑君,等.重金属污染水稻的冠层反射光谱特征研究[J].光谱学与光谱分析,2010,30(2):430-434.
[11]Wang J H,Huang W J,Lao C L,et al.Inversion of winter wheat foliage vertical distribution based on canopy reflected spectrum by partial least squares regression method[J].Spectroscopy&Spectral Analysis,2007,27(7):1319-1322.
[12]仲晓春,戴其根,何理,等.不同浓度镉胁迫下水稻冠层光谱特征及其预测评价[J].农业环境科学学报,2012,31(3):448-454.
[13]陈圣波,周超,王晋年.黑龙江多金属矿区植物胁迫光谱及其与金属元素含量关系研究[J].光谱学与光谱分析,2012,32(5):1310-1315.
[14]曹仕,刘湘南,刘慕霞.基于高光谱指数的水稻砷污染胁迫多重判别模型[J].环境科学,2010,31(10):2462-2468.
[15]Dong J,Dai W,Xu J,et al.Spectral estimation model construction of heavy metals in mining reclamation areas[J].International Journal of Environmental Research&Public Health,2016,13(7):640.
[16]Lopes M S,Reynolds M P.Stay-green in spring wheat can be determined by spectral reflectance measurements(normalized difference vegetation index)independently from phenology[J].Journal of Experimental Botany,2012,63(10):3789-3798.
[17]Devadas R,Lamb D W,Simpfendorfer S,et al.Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves[J].Precision Agriculture,2009,10(6):459-470.
[18]Liu M,Liu X,Wu M,et al.Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model[J].Computers&Geosciences,2011,37(10):1642-1652.
[19]Spielmeyer W,Sharp P J,Lagudah E S.Identification and validation of markers linked to broad-spectrum stem rust resistance gene,in wheat[J].Crop Science,2003,43(1):333-336.
[20]刘素红,刘新会,侯娟,等.植物光谱应用于白菜铜胁迫响应研究[J].中国科学,2007,37(5):693-699.
[21]Ren H Y,Zhuang D F,Yang J X,et al.A feasibility study of NIR spectra in identifying heavy metal contamination in rice around abandoned tailing ponds:A case study in Guiyang county in south China[J].Journal of Geophysics&Remote Sensing,2013,2(2):204.
[22]吉曹翔,李崇,关艳玲,等.基于小波变换的水稻砷污染光谱奇异性研究[J].现代农业科技,2015(3):199-200.
[23]迟光宇,刘新会,刘素红,等.Cu污染与小麦特征光谱相关关系研究[J].光谱学与光谱分析,2006,22(7):62-66.
[24]吴传庆,童庆禧,郑兰芬.地面、图像光谱数据的预处理[J].遥感技术与应用,2005,20(5):506-511.
[25]林海军,张绘芳,高亚琪,等.基于马氏距离法的荒漠树种高光谱识别[J].光谱学与光谱分析,2014,34(12):3358-3362.
[26]邓书斌,陈秋锦,杜会建,等.ENVI遥感图像处理方法[M].2版.北京:高等教育出版社,2014:381-383.
[27]彭杰,迟春明,向红英,等.基于连续统去除法的土壤盐分含量反演研究[J].土壤学报,2014,67(3):459-469.
[28]彭杰,王家强,向红英,等.土壤含盐量与电导率的高光谱反演精度对比研究[J].光谱学与光谱分析,2014,34(2):510-514.
[29]李丙智,李敏夏,周璇,等.苹果树叶片全氮含量高光谱估算模型研究[J].遥感学报,2010,14(4):761-773.
[30]王菲,王集宁,曹文涛,等.山东省焦家式金矿区土壤重金属铬高光谱监测研究[J].光谱学与光谱分析,2017,37(5):1649-1655.
[31]Zhong P,Zhang P,Wang R.Dynamic learning of SMLR for feature selection and classification of hyperspectral data[J].IEEE Geoscience&Remote Sensing Letters,2008,5(2):280-284.
[32]杨可明,汪国平,尤笛,等.重金属铅离子胁迫下玉米叶片光谱弱差信息的DSAT甄别模型[J].光谱学与光谱分析,2016,36(8):2568-2572.
[33]陈思宁,刘新会,侯娟,等.重金属锌胁迫的白菜叶片光谱响应研究[J].光谱学与光谱分析,2007,27(9):1797-1801.