Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration  

作　　者：Mengxue Wan Fan Ya’nan Wentao Jiao Wenyou Hu Mingchao Lyu Weidong Li Chuanrong Zhang Biao Huang 

机构地区：[1]Key Laboratory of Soil Environment and Pollution Remediation,Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China [2]Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085,China [3]Department of Environmental Science,Zhejiang University,Hangzhou 310058,China [4]Guangdong Provincial Academy of Environmental Science,Guangzhou 510045,China [5]Department of Geography,University of Connecticut,Storrs,CT 06269,USA

出　　处：《Journal of Environmental Sciences》2024年第11期88-96,共9页环境科学学报（英文版）

基　　金：supported by the National Key Research and Development Project(No.2020YFC1807405);the China Postdoctoral Science Foundation(No.2021M703301);the Key-Area Research and Development Program of Guangdong Province(No.2020B0202010006);the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2019312).

摘　　要：Conventionally,soil cadmium(Cd)measurements in the laboratory are expensive and timeconsuming,involving complex processes of sample preparation and chemical analysis.This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance(Vis-NIR)spectroscopy and portable X-ray fluorescence spectrometry(PXRF)to estimate regional soil Cd concentration in a time-and cost-savingmanner.The sensor data of Vis-NIR and PXRF,and Cd concentrations of 128 surface soils from Yunnan Province,China,were measured.Outer-product analysis(OPA)was used for synthesizing the sensor data and Granger-Ramanathan averaging(GRA)was applied to fuse the model results.Artificial neural network(ANN)models were built using Vis-NIR data,PXRF data,and OPA data,respectively.Results showed that:(1)ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation;(2)Fusion methods of both OPA and GRA had higher predictive power(R^(2))=0.89,ratios of performance to interquartile range(RPIQ)=4.14,and lower root mean squared error(RMSE)=0.06,in ANN model based on OPA fusion;R^(2)=0.88,RMSE=0.06,and RPIQ=3.53 in GRA model)than those based on either Vis-NIR data or PXRF data.In conclusion,there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

关 键 词：Artificial neural network Outer-product analysis Granger-Ramanathan averaging Soil Cd concentration Fusion method 

分 类 号：X513[环境科学与工程—环境工程]