烟台地区土壤重金属镍高光谱估测模型  

Estimation of Soil Heavy Metal Nickel in Yantai District Based on Hyperspectral Data

作　　者：王凤华[1] 路杰晖 刘志文[1] 王德强[1] 李西灿[2] WANG Feng-hua;LU Jie-hui;LIU Zhi-wen;WANG De-qiang;LI Xi-can(Geological Surveying and Mapping Institute of Shandong Province.Jinan 250002,China;School of Information Science and Engineering/Shandong Agricultural University,Tai'an 271018,China)

机构地区：[1]山东省地质测绘院,山东济南250002 [2]山东农业大学信息科学与工程学院,山东泰安271018

出　　处：《山东农业大学学报（自然科学版）》2019年第1期84-87,共4页Journal of Shandong Agricultural University：Natural Science Edition

基　　金：山东省地矿局地质科技攻关项目(KY201517);山东省自然科学基金项目(ZR2016DM03)

摘　　要：快速监测土壤重金属污染程度,对发展精细农业、保障食品安全和社会经济可持续发展具有重要意义。本文基于山东省烟台市的70个土壤样本数据,首先分析了土壤重金属镍的分组光谱特性;对土壤光谱反射率进行一阶微分、倒数的一阶微分、对数的一阶微分等六种变换并计算出光谱反射率变换值与土壤镍含量的相关系数,根据极大相关性原则选取光谱特征;然后建立基于BP神经网络的土壤重金属镍含量光谱估侧模型;并利用其它2种建模方法对镍含量进行建模,验证BP神经网络模型的有效性。结果表明,土壤光谱反射率随镍含量的升高而降低,呈现负相关性;以(1/R1015)′、(1/R2286)′、(1/ln(R925))′和(1/ln(R1911))′为估测因子,所建镍含量估侧模型的决定系数为R2=0.912,平均相对误差为14.279%。研究表明,利用高光谱技术定量估测土壤镍含量是可行的。Rapid monitoring of heavy metal pollution in soil is of great significance for developing precision agriculture,ensuring food safety and sustainable development of society and economy.Based on the data of 70 soil samples in Yantai city of Shandong Province,this paper first analyzes the spectral characteristics of the heavy metal nickel group spectral characteristics.Then six transformations of soil spectral reflectance such as first order differential,reciprocal first order differential and logarithmic first order differential are adopted,the correlation coefficients between transformed spectral value and soil nickel content were calculated respectively.The spectral characteristics are selected according to the principle of maximum correlation.Finally,BP neural network was used to estimate nickel content of soil heavy metals based on spectral features,and use two other modeling methods to model the nickel content to verify the effectiveness of the BP neural network model.The results showed that the spectral reflectance of soil decreased with the increase of nickel content and showed negative correlation.Take(1/R 1015)′、(1/R 2286)′、(1/ln(R 925))′and(1/ln(R 1911))′as the estimation factors,Using BP neural network model,the determination coefficient of nickel content was 0.912,the average relative error is 14.279%.The research shows that it is feasible to estimate the nickel content directly by hyperspectral technology.

关 键 词：土壤镍含量 高光谱遥感 光谱特征 地积累指数 修正模型 

分 类 号：X131.3[环境科学与工程—环境科学]