Prediction of heavy metal content in soil of cultivated land: Hyperspectral technology at provincial
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Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University;China;College of Plant Science,Tarim University,Alar,;China;Cyrus Tang Center for Sensor Materials and Applications,Zhejiang University;China,Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University

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    Abstract:

    A total of 643 farmland topsoil samples distributed in 36 counties and cities of Zhejiang Province were collected. The correlation between contents of Ni, Cu, As, Hg, Zn, Cr, Cd and Pb and that of organic matter was probed by measuring the reflectance of soil samples in visible-near infrared light band. The characteristic wave bands of heavy metal elements and organic matter were compared. The partial least squares regression (PLSR) model for the content of each heavy metal element was established. The results indicated that Ni and Cr have the best correlation with organic matter, while As has the worst, with the correlation coefficients 0.54, 0.59 and 0.20, respectively. The distance between heavy metal elements and organic matter in the first three principal components loading diagram was inversely proportional to their correlation coefficient. Degree of overlap between different heavy metal elements and organic matter at hyperspectral sensitive band and the positive and negative consistency of regression coefficients varied greatly, the greater the correlation with organic matter is, the higher degree of overlap is, and the better the positive and negative consistency. In PLSR models of heavy metals, models for Ni and Cr performed well in modeling and predicting with a good ability of quantificational prediction, with RPD values of 1.94 and 1.80 separately. The remaining models for other 6 heavy metals could only conduct distinguishing for high and low values with RPD values ranged from 1.00 to 1.4. The results of this study provide certain theoretical assistance and reference for hyperspectral remote sensing monitoring of soil contamination by heavy metals in large-scale areas.

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XIA Fang, PENG Jie, WANG Qian-Long, ZHOU Lian-Qing, SHI Zhou. Prediction of heavy metal content in soil of cultivated land: Hyperspectral technology at provincial[J]. Journal of Infrared and Millimeter Waves,2015,34(5):593~599

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History
  • Received:October 24,2014
  • Revised:February 24,2015
  • Adopted:March 05,2015
  • Online: November 30,2015
  • Published: