基于有机质光谱特征的土壤重金属Pb估算模型研究
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摘要
为实现土壤重金属含量的高光谱快速监测,以石家庄地表水源保护区土壤样本为研究对象,基于土壤有机质(SOM)敏感波段对应的多种光谱变换指标,采用多元线性逐步回归和偏最小二乘回归方法,构建土壤重金属Pb含量的间接反演模型。结果表明:研究区土壤样本Pb平均值为19.729mg/kg,SOM与Pb含量的R达到0.740,两者存在一定的吸附赋存关系。SOM在土壤反射率中的敏感波段为798nm,各种光谱变换指标中土壤反射率倒数对数一阶微分(ATFD)与SOM含量的相关性最大,R达到-0.754。基于建模样本和验证样本分析,多光谱变换指标偏最小二乘回归(M-PLSR)模型优于单光谱变换指标多元线性逐步回归(U-MLSR)模型和多光谱变换指标多元线性逐步回归(M-MLSR)模型,建模样本和验证样本R~2分别为0.876和0.801。基于SOM光谱诊断特征建立M-PLSR模型来间接反演土壤重金属Pb含量是可行的,可为该地区土壤重金属Pb的快速监测提供参考。
To achieve the purpose of measuring heavy metal content rapidly by hyperspectral techniques,estimation models of Pb were developed adopting multiple linear stepwise regression and partial least squares regression methods on the basis of multiple spectral transformations corresponding to the sensitive bands of soil organic matter(SOM).The research was done in the protection zone of surface water source of Shijiazhuang.The results showed that the average content of Pb in soil samples was 19.729 mg/kg,and Rbetween SOM and Pb reached 0.740 on account of the adsorption effect.The 798 nm was regarded as the sensitive band of SOM in the reflectance spectral.Among the various spectral transformations,absorbance transforms first derivative(ATFD)of soil reflecance and SOM content had the strongest correlation,and Rreached-0.754.Based on modeling and validating sample analysis,the multivariate partial least squares regression(M-PLSR)model was superior to the univariate multiple linear stepwise regression(U-MLSR)model and the multivariate multiple linear stepwise regression(M-MLSR)model.The modeling and validating samples' R~2 of M-PLSR were 0.876 and 0.801,respectively.It was feasible for multiple spectral transformations estimation model to estimate the heavy metal Pb content indirectly.M-PLSR model could provide a reference for Pb content monitoring rapidly.
To achieve the purpose of measuring heavy metal content rapidly by hyperspectral techniques,estimation models of Pb were developed adopting multiple linear stepwise regression and partial least squares regression methods on the basis of multiple spectral transformations corresponding to the sensitive bands of soil organic matter(SOM).The research was done in the protection zone of surface water source of Shijiazhuang.The results showed that the average content of Pb in soil samples was 19.729 mg/kg,and Rbetween SOM and Pb reached 0.740 on account of the adsorption effect.The 798 nm was regarded as the sensitive band of SOM in the reflectance spectral.Among the various spectral transformations,absorbance transforms first derivative(ATFD)of soil reflecance and SOM content had the strongest correlation,and Rreached-0.754.Based on modeling and validating sample analysis,the multivariate partial least squares regression(M-PLSR)model was superior to the univariate multiple linear stepwise regression(U-MLSR)model and the multivariate multiple linear stepwise regression(M-MLSR)model.The modeling and validating samples' R~2 of M-PLSR were 0.876 and 0.801,respectively.It was feasible for multiple spectral transformations estimation model to estimate the heavy metal Pb content indirectly.M-PLSR model could provide a reference for Pb content monitoring rapidly.
引文
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[14]郭斗斗,黄绍敏,张水清,等.多种潮土有机质高光谱预测模型的对比分析[J].农业工程学报,2014,30(21):192-200.
[15]张娟娟,田永超,朱艳,等.不同类型土壤的光谱特征及其有机质含量预测[J].中国农业科学,2009,42(9):3154-3163.
[16]江振蓝,杨玉盛,沙晋明.GWR模型在土壤重金属高光谱预测中的应用[J].地理学报,2017,72(3):533-544.
[17]王小攀,郑晓坡,刘福江,等.高光谱遥感土壤有机质含量信息提取与分析[J].地理空间信息,2012,10(5):75-78.
[18]李曦.基于高光谱遥感的土壤有机质预测建模研究[D].杭州:浙江大学,2013.
[19]章涛,于雷.土壤有机质高光谱估算模型研究进展[J].湖北农业科学,2017,56(17):3205-3208.
[2]吴健生,宋静,郑茂坤,等.土壤重金属全量监测方法研究进展[J].东北农业大学学报,2011,42(5):133-139.
[3]KEMPER T,SOMMER S.Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy[J].Environmental Science&Technology,2002,36(12):2742-2747.
[4]徐明星,吴绍华,周生路,等.重金属含量的高光谱建模反演:考古土壤中的应用[J].红外与毫米波学报,2011,30(2):109-114.
[5]涂宇龙,邹滨,姜晓璐,等.矿区土壤Cu含量高光谱反演建模[J].光谱学与光谱分析,2018,38(2):575-581.
[6]ALLOWAY B J.Heavy metals in soils[M].Dordrecht:Springer,2013.
[7]MOROS J,FDEZ ORTIZ DE VALLEJUELO S,GREDILLAA,et al.Use of reflectance infrared spectroscopy for monitoring the metal content of the estuarine sediments of the NerbioiIbaizabal River(Metropolitan Bilbao,Bay of Biscay,Basque Country)[J].Environmental Science&Technology,2009,43(24):9314-9320.
[8]KOOISTRA L,WEHRENS R,LEUVEN R W,et al.Possibilities of visible-near-infrared spectroscopy for the assessment of soil contamination in river floodplains[J].Analytica Chimica Acta,2001,446(1/2):97-105.
[9]贺军亮,蒋建军,周生路,等.土壤有机质含量的高光谱特性及其反演[J].中国农业科学,2007,40(3):638-643.
[10]贺军亮,蒋建军,孙中伟,等.土壤重金属含量光谱估算模型的初步研究[J].农机化研究,2009,31(9):22-25.
[11]卓荦.基于高光谱遥感的土壤重金属空间分布研究[D].武汉:武汉大学,2010.
[12]GANNOUNI S,REBAI N,ABDELJAOUED S.A spectroscopic approach to assess heavy metals contents of the mine waste of Jalta and Bougrine in the North of Tunisia[J].Journal of Geographic Information System,2012,4(3):242-253.
[13]赵晴,刘征,赵旭阳.石家庄地表水源保护区土地利用变化与水质相关性研究[J].水土保持研究,2013,20(2):121-126.
[14]郭斗斗,黄绍敏,张水清,等.多种潮土有机质高光谱预测模型的对比分析[J].农业工程学报,2014,30(21):192-200.
[15]张娟娟,田永超,朱艳,等.不同类型土壤的光谱特征及其有机质含量预测[J].中国农业科学,2009,42(9):3154-3163.
[16]江振蓝,杨玉盛,沙晋明.GWR模型在土壤重金属高光谱预测中的应用[J].地理学报,2017,72(3):533-544.
[17]王小攀,郑晓坡,刘福江,等.高光谱遥感土壤有机质含量信息提取与分析[J].地理空间信息,2012,10(5):75-78.
[18]李曦.基于高光谱遥感的土壤有机质预测建模研究[D].杭州:浙江大学,2013.
[19]章涛,于雷.土壤有机质高光谱估算模型研究进展[J].湖北农业科学,2017,56(17):3205-3208.
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