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Journal of Applied Remote Sensing • Open Access

Quantitative estimation of concentrations of dissolved rare earth elements using reflectance spectroscopy
Author(s): Jingjing Dai; Denghong Wang; Runsheng Wang; Zhenghui Chen

Paper Abstract

Characteristic spectral parameters such as the wavelength and depth of absorption bands are widely used to quantitatively estimate the composition of samples from hyperspectral reflectance data in soil science, mineralogy as well as vegetation study. However, little research has been conducted on the spectral characteristic of rare earth elements (REE) and their relationship with chemical composition of aqueous solutions. Reflectance spectra of ore leachate solutions and contaminated stream water from a few REE mines in the Jiangxi Province, China, are studied for the first time in this work. The results demonstrate that the six diagnostic absorption features of the rare earths are recognized in visible and near-infrared wavelengths at 574, 790, 736, 520, 861, and 443 nm. The intensity of each of these six absorption bands is linearly correlated with the abundance of total REE, with the r2 value <0.95 and the detection limit at ≥75,000  μg/L . It is suggested that reflectance spectroscopy provides an ideal routine analytical tool for characterizing leachate samples. The outcome of this study also has implications for monitoring the environmental effect of REE mining, in particular in stream water systems by hyperspectral remote sensing.

Paper Details

Date Published: 28 August 2013
PDF: 14 pages
J. Appl. Remote Sens. 7(1) 073513 doi: 10.1117/1.JRS.7.073513
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Jingjing Dai, China Aero Geophysical Survey & Remote Sensing Ctr. for Land and Resources (China)
Denghong Wang, China Aero Geophysical Survey & Remote Sensing Ctr. for Land and Resources (China)
Runsheng Wang, China Aero Geophysical Survey & Remote Sensing Ctr. for Land and Resources (China)
Zhenghui Chen, China Aero Geophysical Survey & Remote Sensing Ctr. for Land and Resources (China)


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