In this article, we propose that reflectance spectroscopy or remote sensing technology is a rapid and inexpensive tool to monitor heavy-metal contamination in soils. We analyzed three data sets by both chemical and reflectance spectroscopy methods. Despite the data sets being obtained from different locations and at different times, all three data sets gave similar results; that is, those that were well correlated with Fe also had higher predictive accuracy: Ni, Cr, and Co in Baguazhou Island; Ni, Cr, and Cu in Jiangning County; Ni, Cu, and As in some areas of Baguazhou Island had close correlations with Fe, with greater R 2. Cd and Pb in Baguazhou Island; Cd, As, and Hg in Jiangning County; Hg, Pb, and Cd in some areas of Baguazhou Island had low correlations with Fe. The predictions of these elements were also the least among all the elements. The same results were found for the prediction of global calibration. The consistent trends acquired by the four performances, that is, the order of prediction accuracy for contaminant elements was approximately the same as the order of their correlation coefficients with Fe, strongly suggesting that the successful prediction of the spectrally featureless contaminant elements by reflectance spectra is not only statistically coincident, but representative of an underlying or general relationship between the spectra and the elements. The relationship is indicative of a physical mechanism between reflectance spectra and trace elements. The F statistic shows that the performance of the simulated HyMap, TM, and QuickBird bands also gave satisfactory results. These two observations demonstrate that heavy-metal contamination can be monitored successfully using remote sensing technology.
1The MOE Key Lab of Coast and Island Development, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, China. Dr. Yunzhao Wu is corresponding author. E-mail: email@example.com
2State Key Laboratory for Mineral Deposits Research, Department of Earth Sciences, Nanjing University, Nanjing, China. 3State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China. 4Geological Survey of Jiangsu Province, Nanjing, China.
Received May 23, 2010.
Accepted for publication January 19, 2011.