Full text

Turn on search term navigation

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

Details

Title
vis–NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil
Author
Gholizadeh, Asa 1   VIAFID ORCID Logo  ; Coblinski, João A 1   VIAFID ORCID Logo  ; Saberioon, Mohammadmehdi 2   VIAFID ORCID Logo  ; Ben-Dor, Eyal 3   VIAFID ORCID Logo  ; Drábek, Ondřej 1   VIAFID ORCID Logo  ; Demattê, José A M 4 ; Borůvka, Luboš 1   VIAFID ORCID Logo  ; Němeček, Karel 1   VIAFID ORCID Logo  ; Chabrillat, Sabine 2   VIAFID ORCID Logo  ; Dajčl, Julie 1 

 Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, 16500 Prague, Czech Republic; [email protected] (J.A.C.); [email protected] (O.D.); [email protected] (L.B.); [email protected] (K.N.); [email protected] (J.D.) 
 Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany; [email protected] (M.S.); [email protected] (S.C.) 
 Remote Sensing Laboratory, Department of Geography and Human Environment, Porter School of Environment and Earth Science, Tel Aviv University, Tel Aviv 69978, Israel; [email protected] 
 Department of Soil Science, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Padua Dias Avenue, 11, CP 9, Piracicaba 13418-900, Brazil; [email protected] 
First page
2386
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2550332863
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.