Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

查看全文 作　　者：REN Hong-[1,2,3]Yan;ZHUANG Da-[1,2]Fang;A. N. [4]SINGH;PAN Jian-[1]Jun;QIU Dong-[2,3]Sheng;SHI Run-[5]He

机构地区：[1]College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095 (China);[2]Resource and Environmental Science Data Center, Chinese Academy of Sciences, Beijing 100101 (China);[3]Key Laboratory of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing 100081;[4]Department of Botany Panjab University, Chandigarh-160014 (India);[5]Key Laboratory of Geographic Information Science for Ministry of Education, East China Normal University, Shanghai 200062 (China)

出　　处：《Pedosphere》索引2009年第19卷第6期,共8页

基　　金：Project supported by the National Natural Science Foundation of China (No. 40571130);the Natural Science Foundation of Shanghai, China (No. 07ZR14032) 摘　　要：Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination. 关键词：农业土壤反射光谱铜污染偏最小二乘回归土壤重金属含量数据预处理光谱反射率 Devices

相关文献

参考文献（31）
引证文献（33）
耦合文献（760）

参考文献(31)

引证文献(33)

耦合文献(760)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

参考文献(31)

引证文献(33)

耦合文献(760)

用户提示