微分光谱变换方法对土壤重金属含量反演精度的影响研究  

Effect of Differential Spectral Transformation on Soil HeavyMetal Content Inversion Accuracy

作　　者：白宗璠 韩玲[1] 姜旭海 武春林 BAI Zong-fan;HAN Ling;JIANG Xu-hai;WU Chun-lin(School of Land Engineering,Chang'an University,Xi'an 710075,China;Northwest Nonferrous Geological Mining Group Co.,Ltd.,Xi'an 710054,China)

机构地区：[1]长安大学土地工程学院,陕西西安710075 [2]西北有色地质矿业集团有限公司,陕西西安710054

出　　处：《光谱学与光谱分析》2024年第5期1449-1456,共8页Spectroscopy and Spectral Analysis

基　　金：国家科技重大专项(04-H30G01-9001-20/22);国家自然科学基金项目(211035210511)资助。

摘　　要：随着我国工农业的日益发展,土壤中以镍(Ni)、铁(Fe)、铜(Cu)、铬(Cr)、铅(Pb)等为代表的重金属污染对人类生活产生了严重影响。高光谱遥感技术具有实时、无损、快速等优点,为高效准确地获取土壤重金属含量提供了科学手段。而在利用高光谱数据反演土壤重金属含量时,微分光谱变换方法的选择对遥感反演土壤重金属含量的精度有显著影响。为明确二者关系,基于研究区采集的60个土壤样品,测定其Ni、Fe、Cr、Cu、Pb等含量以及350~2500 nm波段范围的光谱反射率。在相关系数(CC)分析法的基础上通过改进离散粒子群算法(MDBPSO)优选遥感探测土壤重金属含量的特征波段。最终以优选出的特征波段作为自变量利用随机森林(RF)算法构建了Ni、Fe、Cr、Cu、Pb等重金属含量的估测模型。在对原始反射率数据进行高斯平滑的基础上,对比分析了一阶微分(R′)、对数倒数的一阶微分(1/lgR)′、倒数的一阶微分(1/R)′、指数的一阶微分(e^(R))′四种微分光谱变换方法对土壤重金属反演精度的影响。结果表明,在CC分析法的基础上,MDBPSO算法可以有效地降低光谱数据的冗余度,提高模型的运行效率。其中R′、(1/lgR)′、(1/R)′、(e^(R))′中对Ni、Fe、Cr、Cu、Pb敏感的特征波段个数分别至少减少了154、363、135、744和889个。(1/lgR)′、R′、R′、(1/R)′、R′光谱变换方法分别应用到Ni、Fe、Cr、Cu、Pb特征波段的组合运算中,得到的估测模型的精度优于其他微分变换方法;模型检验集的决定系数分别为0.913、0.906、0.872、0.912、0.876,均方根误差分别为0.743、0.095、2.588、1.541、1.453。本研究为利用遥感数据反演土壤重金属含量微分光谱变换方法的选择提供了科学的参考,为进一步实现土壤重金属含量的大面积高精度遥感监测提供新的思路。With the increasing development of industry and agriculture in China,heavy metal pollution in soil represented by nickel(Ni),iron(Fe),copper(Cu),chromium(Cr),lead(Pb),etc.,has a serious impact on human life.Hyperspectral technology has advantages such as being real-time,non-destructive,and fast,which provides scientific means to obtain information on soil heavy metal content efficiently and accurately.At the same time,the spectral transformation method significantly impacts the inversion accuracy of soil heavy metal content.To clarify the relationship between the spectral transformation method and the inversion accuracy,60 soil samples were collected in the study area to determine the Ni,Fe,Cr,Cu,and Pb heavy metals content and the corresponding spectral reflectance between 350~2500 nm.Based on the correlation coefficient(CC)analysis,the feature bands for remote sensing detection of soil heavy metals were selected by the modified discrete binary particle swarm optimization(MDBPSO)method.Finally,the inverse models of Ni,Fe,Cr,Cu and Pb contents were constructed by the random forest(RF)algorithm with the feature bands as independent variables.In this study,based on Gaussian smoothing of the original reflectance,the effects of four differential spectral transformation methods,including first-order differential(R′),first-order differential of logarithmic inverse(1/lg R)′,first-order differential of inverse(1/R)′,and first-order differential of exponential(e^(R))′,on the accuracy of soil heavy metal inversion were compared and analyzed.The results show that based on the CC analysis method,the MDBPSO algorithm can effectively reduce the redundancy of spectral data and improve the efficiency of the model operation.The number of feature bands sensitive to Ni,Fe,Cr,Cu and Pb in R′,(1/lg R)′,(1/R)′,(e^(R))′,has been reduced by at least 154,363,135,744 and 889,respectively.(1/lg R)′,R′,R′,(1/R)′,and R′spectral transformation methods were applied to the combined operation of Ni,Fe,Cr,Cu,and Pb featu

关 键 词：遥感 高光谱 土壤 光谱变换方法 重金属 改进离散粒子群 随机森林 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]