TY - JOUR
T1 - Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom
AU - Lamine, Salim
AU - Petropoulos, George
AU - Brewer, Paul
AU - Bachari, Nour-El-Islam
AU - Srivastava, Prashant K.
AU - Manevski, Kiril
AU - Kalaitzidis, Chariton
AU - Macklin, Mark
N1 - Funding Information:
Funding: This research was funded by the Erasmus Training Programme in Aberystwyth University; G.P.P.’s contribution was supported by the FP7-People Project ENViSIon-EO, reference number 334533; S.L. gratefully acknowledges the financial support provided by the European Commission.
Funding Information:
This research was funded by the Erasmus Training Programme in Aberystwyth University; G.P.P.’s contribution was supported by the FP7-People Project ENViSIon-EO, reference number 334533; S.L. gratefully acknowledges the financial support provided by the European Commission.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/2/13
Y1 - 2019/2/13
N2 - Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field-and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field-and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field-and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.
AB - Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field-and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field-and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field-and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.
KW - hyperspectral data
KW - heavy metals
KW - floodplain
KW - soil spectral library
KW - regression modelling
KW - Hyperspectral data
KW - Soil spectral library
KW - Floodplain
KW - Heavy metals
KW - Regression modelling
UR - http://www.scopus.com/inward/record.url?scp=85061861272&partnerID=8YFLogxK
U2 - 10.3390/s19040762
DO - 10.3390/s19040762
M3 - Article
C2 - 30781812
SN - 1424-8220
VL - 19
JO - Sensors
JF - Sensors
IS - 4
M1 - 762
ER -