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 usi...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.展开更多
基金supported by the National Key Research and Development Project(No.2020YFC1807405)the China Postdoctoral Science Foundation(No.2021M703301)+1 种基金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).
文摘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.
