Pedosphere 19(6): 719--726, 2009
ISSN 1002-0160/CN 32-1315/P
©2009 Soil Science Society of China
Published by Elsevier B.V. and Science Press
Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study
REN Hong-Yan1,2,3, ZHUANG Da-Fang1,2, A. N. SINGH4, PAN Jian-Jun1, QIU Dong-Sheng2,3 and SHI Run-He5
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 (China)
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)
      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 (R2). According to the criteria of minimal RRMSE and maximal R2, the PLSR models with the FD pretreatment (RRMSE = 0.24, R2 = 0.61), SNV pretreatment (RRMSE = 0.08, R2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R2 = 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.
Key Words:  data pre-processing, heavy metal, regression models, soil iron, spectral reflectance
Citation: Ren, H. Y., Zhuang, D. F., Singh, A. N., Pan, J. J., Qiu, D. S. and Shi, R. H. 2009. Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study. Pedosphere. 19(6): 719-726.
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