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Title: Spectroradiometry as a tool for monitoring soil contamination by heavy metals in a floodplain site
Authors: Lamine, Salim
Manish, KumarPandey
Petropoulos, George P.
Brewer, Paul A.
Srivastava, Prashant
Manevski, Kiril
Toulios, Leonidas
Bachari, Nour-El-Islam
Macklin, Mark G.
Keywords: Hyperspectral data
heavy metals
soil spectral library
regression modeling
Issue Date: 2020
Publisher: Université de Bouira
Citation: Hyperspectral Remote Sensing Theory and Applications, Pages 249-268
Abstract: Soil contamination by heavy metals is common in floodplains throughout the world. Apart from other assessment techniques available, hyperspectral remote sensing is widely used as it offers a lucrative and fast assessment. The current work explores the possibility of on-field and laboratory spectroradiometry investigations together with geochemical data of lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd) in quantifying and modeling heavy metal soil contamination (HMSC) for a floodplain situated in Wales, United Kingdom. The goal of the study was to (1) gather on-field- as well as lab-based spectra from contaminated soils using analytical spectral devices FieldSpec3, in the spectrum range of 350–2500 nm; (2) construct spectral libraries of on-field- as well as lab-based readings; (3) carry out geochemical analyses of Pb, Zn, Cu, and Cd with the help of an atomic absorption spectrometer; (4) recognize the explicit spectral areas accompanying the modeling of HMSC; and (5) develop and validate heavy metal prediction models (HMPMs) through the spectral features of the contaminants and their concentrations in the soil. Two spectral libraries were developed from the on-field- and lab-based spectral features, which were derived from 85 soil samples. These spectral libraries along with the concentrations of Pb, Zn, Cu, and Cd were joint to construct eight HMPMs by stepwise multiple linear regression. The output provided, for the first time, the viability to predict HMSC in a highly contaminated floodplain site through the combination of geochemical analyses and field spectroradiometry. The resultant model offered support for mapping heavy metal concentrations over a huge area using space-borne hyperspectral sensors.
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