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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom</title><author><name sortKey="Lamine, Salim" sort="Lamine, Salim" uniqKey="Lamine S" first="Salim" last="Lamine">Salim Lamine</name><affiliation><nlm:aff id="af1-sensors-19-00762">Faculty of Natural Sciences, Life and Earth Sciences, University Akli Mohand Oulhadj of Bouira, 10000 Bouira, Algeria</nlm:aff></affiliation><affiliation><nlm:aff id="af2-sensors-19-00762">Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales SY23 3DB, UK;<email>pqb@aber.ac.uk</email></nlm:aff></affiliation></author><author><name sortKey="Petropoulos, George P" sort="Petropoulos, George P" uniqKey="Petropoulos G" first="George P." last="Petropoulos">George P. Petropoulos</name><affiliation><nlm:aff id="af3-sensors-19-00762">Department of Soil and Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1, Theofrastou St., 41335 Larisa, Greece;<email>petropoulos.george@gmail.com</email></nlm:aff></affiliation><affiliation><nlm:aff id="af4-sensors-19-00762">School of Mineral and Resources Engineering, Technical University of Crete, Kounoupidiana Campus, 73100 Crete, Greece</nlm:aff></affiliation></author><author><name sortKey="Brewer, Paul A" sort="Brewer, Paul A" uniqKey="Brewer P" first="Paul A." last="Brewer">Paul A. Brewer</name><affiliation><nlm:aff id="af2-sensors-19-00762">Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales SY23 3DB, UK;<email>pqb@aber.ac.uk</email></nlm:aff></affiliation></author><author><name sortKey="Bachari, Nour El Islam" sort="Bachari, Nour El Islam" uniqKey="Bachari N" first="Nour-El-Islam" last="Bachari">Nour-El-Islam Bachari</name><affiliation><nlm:aff id="af5-sensors-19-00762">Faculty of Biological Sciences, University of Sciences and Technology Houari Boumediene, BP 32, El Alia, Bab Ezzouar 16111, Algeria;<email>bacharinouri@gmail.com</email></nlm:aff></affiliation></author><author><name sortKey="Srivastava, Prashant K" sort="Srivastava, Prashant K" uniqKey="Srivastava P" first="Prashant K." last="Srivastava">Prashant K. Srivastava</name><affiliation><nlm:aff id="af6-sensors-19-00762">Institute of Environment and Sustainable Development & DST Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi 221005, India;<email>prashant.just@gmail.com</email></nlm:aff></affiliation></author><author><name sortKey="Manevski, Kiril" sort="Manevski, Kiril" uniqKey="Manevski K" first="Kiril" last="Manevski">Kiril Manevski</name><affiliation><nlm:aff id="af7-sensors-19-00762">Department of Agroecology, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark;<email>kiril.manevski@agrsci.dk</email></nlm:aff></affiliation></author><author><name sortKey="Kalaitzidis, Chariton" sort="Kalaitzidis, Chariton" uniqKey="Kalaitzidis C" first="Chariton" last="Kalaitzidis">Chariton Kalaitzidis</name><affiliation><nlm:aff id="af8-sensors-19-00762">Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania, 73100 Crete, Greece;<email>chariton@maich.gr</email></nlm:aff></affiliation></author><author><name sortKey="Macklin, Mark G" sort="Macklin, Mark G" uniqKey="Macklin M" first="Mark G." last="Macklin">Mark G. Macklin</name><affiliation><nlm:aff id="af9-sensors-19-00762">School of Geography, College of Science, University of Lincoln, Brayford Pool, Lincoln, Lincolnshire LN6 7TS, UK;<email>mmacklin@lincoln.ac.uk</email></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">30781812</idno><idno type="pmc">6413008</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413008</idno><idno type="RBID">PMC:6413008</idno><idno type="doi">10.3390/s19040762</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000280</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000280</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom</title><author><name sortKey="Lamine, Salim" sort="Lamine, Salim" uniqKey="Lamine S" first="Salim" last="Lamine">Salim Lamine</name><affiliation><nlm:aff id="af1-sensors-19-00762">Faculty of Natural Sciences, Life and Earth Sciences, University Akli Mohand Oulhadj of Bouira, 10000 Bouira, Algeria</nlm:aff></affiliation><affiliation><nlm:aff id="af2-sensors-19-00762">Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales SY23 3DB, UK;<email>pqb@aber.ac.uk</email></nlm:aff></affiliation></author><author><name sortKey="Petropoulos, George P" sort="Petropoulos, George P" uniqKey="Petropoulos G" first="George P." last="Petropoulos">George P. Petropoulos</name><affiliation><nlm:aff id="af3-sensors-19-00762">Department of Soil and Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1, Theofrastou St., 41335 Larisa, Greece;<email>petropoulos.george@gmail.com</email></nlm:aff></affiliation><affiliation><nlm:aff id="af4-sensors-19-00762">School of Mineral and Resources Engineering, Technical University of Crete, Kounoupidiana Campus, 73100 Crete, Greece</nlm:aff></affiliation></author><author><name sortKey="Brewer, Paul A" sort="Brewer, Paul A" uniqKey="Brewer P" first="Paul A." last="Brewer">Paul A. Brewer</name><affiliation><nlm:aff id="af2-sensors-19-00762">Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales SY23 3DB, UK;<email>pqb@aber.ac.uk</email></nlm:aff></affiliation></author><author><name sortKey="Bachari, Nour El Islam" sort="Bachari, Nour El Islam" uniqKey="Bachari N" first="Nour-El-Islam" last="Bachari">Nour-El-Islam Bachari</name><affiliation><nlm:aff id="af5-sensors-19-00762">Faculty of Biological Sciences, University of Sciences and Technology Houari Boumediene, BP 32, El Alia, Bab Ezzouar 16111, Algeria;<email>bacharinouri@gmail.com</email></nlm:aff></affiliation></author><author><name sortKey="Srivastava, Prashant K" sort="Srivastava, Prashant K" uniqKey="Srivastava P" first="Prashant K." last="Srivastava">Prashant K. Srivastava</name><affiliation><nlm:aff id="af6-sensors-19-00762">Institute of Environment and Sustainable Development & DST Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi 221005, India;<email>prashant.just@gmail.com</email></nlm:aff></affiliation></author><author><name sortKey="Manevski, Kiril" sort="Manevski, Kiril" uniqKey="Manevski K" first="Kiril" last="Manevski">Kiril Manevski</name><affiliation><nlm:aff id="af7-sensors-19-00762">Department of Agroecology, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark;<email>kiril.manevski@agrsci.dk</email></nlm:aff></affiliation></author><author><name sortKey="Kalaitzidis, Chariton" sort="Kalaitzidis, Chariton" uniqKey="Kalaitzidis C" first="Chariton" last="Kalaitzidis">Chariton Kalaitzidis</name><affiliation><nlm:aff id="af8-sensors-19-00762">Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania, 73100 Crete, Greece;<email>chariton@maich.gr</email></nlm:aff></affiliation></author><author><name sortKey="Macklin, Mark G" sort="Macklin, Mark G" uniqKey="Macklin M" first="Mark G." last="Macklin">Mark G. Macklin</name><affiliation><nlm:aff id="af9-sensors-19-00762">School of Geography, College of Science, University of Lincoln, Brayford Pool, Lincoln, Lincolnshire LN6 7TS, UK;<email>mmacklin@lincoln.ac.uk</email></nlm:aff></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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<sup>®</sup> 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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Johnston, D" uniqKey="Johnston D">D. Johnston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Foulds, S A" uniqKey="Foulds S">S.A. Foulds</name></author><author><name sortKey="Brewer, P A" uniqKey="Brewer P">P.A. Brewer</name></author><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author><author><name sortKey="Haresign, W" uniqKey="Haresign W">W. Haresign</name></author><author><name sortKey="Betson, R E" uniqKey="Betson R">R.E. Betson</name></author><author><name sortKey="Rassner, S M E" uniqKey="Rassner S">S.M.E. Rassner</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author><author><name sortKey="Hudson Edwards, K A" uniqKey="Hudson Edwards K">K.A. Hudson-Edwards</name></author><author><name sortKey="Dawson, E J" uniqKey="Dawson E">E.J. Dawson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author><author><name sortKey="Brewer, P A" uniqKey="Brewer P">P.A. Brewer</name></author><author><name sortKey="Hudson Edwards, K A" uniqKey="Hudson Edwards K">K.A. Hudson-Edwards</name></author><author><name sortKey="Bird, G" uniqKey="Bird G">G. Bird</name></author><author><name sortKey="Coulthard, T J" uniqKey="Coulthard T">T.J. Coulthard</name></author><author><name sortKey="Dennis, I A" uniqKey="Dennis I">I.A. Dennis</name></author><author><name sortKey="Lechler, P J" uniqKey="Lechler P">P.J. Lechler</name></author><author><name sortKey="Miller, J R" uniqKey="Miller J">J.R. Miller</name></author><author><name sortKey="Turner, J N" uniqKey="Turner J">J.N. Turner</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mayes, W M" uniqKey="Mayes W">W.M. Mayes</name></author><author><name sortKey="Potter, H A B" uniqKey="Potter H">H.A.B. Potter</name></author><author><name sortKey="Jarvis, A P" uniqKey="Jarvis A">A.P. Jarvis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gozzard, E" uniqKey="Gozzard E">E. Gozzard</name></author><author><name sortKey="Mayes, W M" uniqKey="Mayes W">W.M. Mayes</name></author><author><name sortKey="Potter, H A B" uniqKey="Potter H">H.A.B. Potter</name></author><author><name sortKey="Jarvis, A P" uniqKey="Jarvis A">A.P. Jarvis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Henke, J M" uniqKey="Henke J">J.M. Henke</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, Y" uniqKey="Liu Y">Y. Liu</name></author><author><name sortKey="Wen, C" uniqKey="Wen C">C. Wen</name></author><author><name sortKey="Liu, X" uniqKey="Liu X">X. Liu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Luo, X S" uniqKey="Luo X">X.-S. Luo</name></author><author><name sortKey="Yu, S" uniqKey="Yu S">S. Yu</name></author><author><name sortKey="Zhu, Y G" uniqKey="Zhu Y">Y.-G. Zhu</name></author><author><name sortKey="Li, X D" uniqKey="Li X">X.-D. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Choe, E" uniqKey="Choe E">E. Choe</name></author><author><name sortKey="Kim, K W" uniqKey="Kim K">K.-W. Kim</name></author><author><name sortKey="Bang, S" uniqKey="Bang S">S. Bang</name></author><author><name sortKey="Yoon, I H" uniqKey="Yoon I">I.-H. Yoon</name></author><author><name sortKey="Lee, K Y" uniqKey="Lee K">K.-Y. Lee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cai, Q Y" uniqKey="Cai Q">Q.-Y. Cai</name></author><author><name sortKey="Mo, C H" uniqKey="Mo C">C.-H. Mo</name></author><author><name sortKey="Li, H Q" uniqKey="Li H">H.-Q. Li</name></author><author><name sortKey="Lu, H" uniqKey="Lu H">H. Lü</name></author><author><name sortKey="Zeng, Q Y" uniqKey="Zeng Q">Q.-Y. Zeng</name></author><author><name sortKey="Li, Y W" uniqKey="Li Y">Y.-W. Li</name></author><author><name sortKey="Wu, X L" uniqKey="Wu X">X.-L. Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Al Maliki, A" uniqKey="Al Maliki A">A. Al Maliki</name></author><author><name sortKey="Bruce, D" uniqKey="Bruce D">D. Bruce</name></author><author><name sortKey="Owens, G" uniqKey="Owens G">G. Owens</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pandit, C M" uniqKey="Pandit C">C.M. Pandit</name></author><author><name sortKey="Filippelli, G M" uniqKey="Filippelli G">G.M. Filippelli</name></author><author><name sortKey="Li, L" uniqKey="Li L">L. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Gupta, M" uniqKey="Gupta M">M. Gupta</name></author><author><name sortKey="Mukherjee, S" uniqKey="Mukherjee S">S. Mukherjee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Han, D" uniqKey="Han D">D. Han</name></author><author><name sortKey="Gupta, M" uniqKey="Gupta M">M. Gupta</name></author><author><name sortKey="Mukherjee, S" uniqKey="Mukherjee S">S. Mukherjee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sharma, N K" uniqKey="Sharma N">N.K. Sharma</name></author><author><name sortKey="Bhardwaj, S" uniqKey="Bhardwaj S">S. Bhardwaj</name></author><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Thanki, Y J" uniqKey="Thanki Y">Y.J. Thanki</name></author><author><name sortKey="Gadhia, P K" uniqKey="Gadhia P">P.K. Gadhia</name></author><author><name sortKey="Gadhia, M" uniqKey="Gadhia M">M. Gadhia</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Choe, E" uniqKey="Choe E">E. Choe</name></author><author><name sortKey="Van Der Meer, F" uniqKey="Van Der Meer F">F. van der Meer</name></author><author><name sortKey="Van Ruitenbeek, F" uniqKey="Van Ruitenbeek F">F. van Ruitenbeek</name></author><author><name sortKey="Van Der Werff, H" uniqKey="Van Der Werff H">H. van der Werff</name></author><author><name sortKey="De Smeth, B" uniqKey="De Smeth B">B. de Smeth</name></author><author><name sortKey="Kim, K W" uniqKey="Kim K">K.-W. Kim</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Djoki, B V" uniqKey="Djoki B">B.V. Djokić</name></author><author><name sortKey="Jovi, V" uniqKey="Jovi V">V. Jović</name></author><author><name sortKey="Jovanovi, M" uniqKey="Jovanovi M">M. Jovanović</name></author><author><name sortKey=" Iri, A" uniqKey=" Iri A">A. Ćirić</name></author><author><name sortKey="Jovanovi, D" uniqKey="Jovanovi D">D. Jovanović</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, B" uniqKey="Zhang B">B. Zhang</name></author><author><name sortKey="Wu, D" uniqKey="Wu D">D. Wu</name></author><author><name sortKey="Zhang, L" uniqKey="Zhang L">L. Zhang</name></author><author><name sortKey="Jiao, Q" uniqKey="Jiao Q">Q. Jiao</name></author><author><name sortKey="Li, Q" uniqKey="Li Q">Q. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Farrand, W H" uniqKey="Farrand W">W.H. Farrand</name></author><author><name sortKey="Harsanyi, J C" uniqKey="Harsanyi J">J.C. Harsanyi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ferrier, G" uniqKey="Ferrier G">G. Ferrier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lamine, S" uniqKey="Lamine S">S. Lamine</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author><author><name sortKey="Singh, S K" uniqKey="Singh S">S.K. Singh</name></author><author><name sortKey="Szab, S" uniqKey="Szab S">S. Szabó</name></author><author><name sortKey="Bachari, N E I" uniqKey="Bachari N">N.E.I. Bachari</name></author><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Suman, S" uniqKey="Suman S">S. Suman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="El Islam, B N" uniqKey="El Islam B">B.N. El Islam</name></author><author><name sortKey="Fouzia, H" uniqKey="Fouzia H">H. Fouzia</name></author><author><name sortKey="Khalid, A" uniqKey="Khalid A">A. Khalid</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Meharrar, K" uniqKey="Meharrar K">K. Meharrar</name></author><author><name sortKey="Bachari, N E I" uniqKey="Bachari N">N.E.I. Bachari</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, M" uniqKey="Liu M">M. Liu</name></author><author><name sortKey="Liu, X" uniqKey="Liu X">X. Liu</name></author><author><name sortKey="Li, J" uniqKey="Li J">J. Li</name></author><author><name sortKey="Li, T" uniqKey="Li T">T. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="You, D" uniqKey="You D">D. You</name></author><author><name sortKey="Zhou, J" uniqKey="Zhou J">J. Zhou</name></author><author><name sortKey="Wang, J" uniqKey="Wang J">J. Wang</name></author><author><name sortKey="Ma, Z" uniqKey="Ma Z">Z. Ma</name></author><author><name sortKey="Pan, L" uniqKey="Pan L">L. Pan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Singh, S K" uniqKey="Singh S">S.K. Singh</name></author><author><name sortKey="Gupta, M" uniqKey="Gupta M">M. Gupta</name></author><author><name sortKey="Thakur, J K" uniqKey="Thakur J">J.K. Thakur</name></author><author><name sortKey="Mukherjee, S" uniqKey="Mukherjee S">S. Mukherjee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lamine, S" uniqKey="Lamine S">S. Lamine</name></author><author><name sortKey="Brewer, P A" uniqKey="Brewer P">P.A. Brewer</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author><author><name sortKey="Kalaitzidis, C" uniqKey="Kalaitzidis C">C. Kalaitzidis</name></author><author><name sortKey="Manevski, K" uniqKey="Manevski K">K. Manevski</name></author><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author><author><name sortKey="Haresign, W" uniqKey="Haresign W">W. Haresign</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pandley, P C" uniqKey="Pandley P">P.C. Pandley</name></author><author><name sortKey="Manevski, K" uniqKey="Manevski K">K. Manevski</name></author><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rosero Vlasova, O A" uniqKey="Rosero Vlasova O">O.A. Rosero-Vlasova</name></author><author><name sortKey="Perez Cabello, F" uniqKey="Perez Cabello F">F. Pérez-Cabello</name></author><author><name sortKey="Montorio Lloveria, R" uniqKey="Montorio Lloveria R">R. Montorio Llovería</name></author><author><name sortKey="Vlassova, L" uniqKey="Vlassova L">L. Vlassova</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Summers, D" uniqKey="Summers D">D. Summers</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ben Dor, E" uniqKey="Ben Dor E">E. Ben-Dor</name></author><author><name sortKey="Patkin, K" uniqKey="Patkin K">K. Patkin</name></author><author><name sortKey="Banin, A" uniqKey="Banin A">A. Banin</name></author><author><name sortKey="Karnieli, A" uniqKey="Karnieli A">A. Karnieli</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, Y" uniqKey="Wu Y">Y. Wu</name></author><author><name sortKey="Chen, J" uniqKey="Chen J">J. Chen</name></author><author><name sortKey="Wu, X" uniqKey="Wu X">X. Wu</name></author><author><name sortKey="Tian, Q" uniqKey="Tian Q">Q. Tian</name></author><author><name sortKey="Ji, J" uniqKey="Ji J">J. Ji</name></author><author><name sortKey="Qin, Z" uniqKey="Qin Z">Z. Qin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ren, H Y" uniqKey="Ren H">H.-Y. Ren</name></author><author><name sortKey="Zhuang, D F" uniqKey="Zhuang D">D.-F. Zhuang</name></author><author><name sortKey="Singh, A N" uniqKey="Singh A">A.N. Singh</name></author><author><name sortKey="Pan, J J" uniqKey="Pan J">J.-J. Pan</name></author><author><name sortKey="Qiu, D S" uniqKey="Qiu D">D.-S. Qiu</name></author><author><name sortKey="Shi, R H" uniqKey="Shi R">R.-H. Shi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Horta, A" uniqKey="Horta A">A. Horta</name></author><author><name sortKey="Malone, B" uniqKey="Malone B">B. Malone</name></author><author><name sortKey="Stockmann, U" uniqKey="Stockmann U">U. Stockmann</name></author><author><name sortKey="Minasny, B" uniqKey="Minasny B">B. Minasny</name></author><author><name sortKey="Bishop, T F A" uniqKey="Bishop T">T.F.A. Bishop</name></author><author><name sortKey="Mcbratney, A B" uniqKey="Mcbratney A">A.B. McBratney</name></author><author><name sortKey="Pallasser, R" uniqKey="Pallasser R">R. Pallasser</name></author><author><name sortKey="Pozza, L" uniqKey="Pozza L">L. Pozza</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nocita, M" uniqKey="Nocita M">M. Nocita</name></author><author><name sortKey="Stevens, A" uniqKey="Stevens A">A. Stevens</name></author><author><name sortKey="Van Wesemael, B" uniqKey="Van Wesemael B">B. van Wesemael</name></author><author><name sortKey="Aitkenhead, M" uniqKey="Aitkenhead M">M. Aitkenhead</name></author><author><name sortKey="Bachmann, M" uniqKey="Bachmann M">M. Bachmann</name></author><author><name sortKey="Barthes, B" uniqKey="Barthes B">B. Barthès</name></author><author><name sortKey="Ben Dor, E" uniqKey="Ben Dor E">E. Ben Dor</name></author><author><name sortKey="Brown, D J" uniqKey="Brown D">D.J. Brown</name></author><author><name sortKey="Clairotte, M" uniqKey="Clairotte M">M. Clairotte</name></author><author><name sortKey="Csorba, A" uniqKey="Csorba A">A. Csorba</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Song, L" uniqKey="Song L">L. Song</name></author><author><name sortKey="Jian, J" uniqKey="Jian J">J. Jian</name></author><author><name sortKey="Tan, D J" uniqKey="Tan D">D.-J. Tan</name></author><author><name sortKey="Xie, H B" uniqKey="Xie H">H.-B. Xie</name></author><author><name sortKey="Luo, Z F" uniqKey="Luo Z">Z.-F. Luo</name></author><author><name sortKey="Gao, B" uniqKey="Gao B">B. Gao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Soriano Disla, J M" uniqKey="Soriano Disla J">J.M. Soriano-Disla</name></author><author><name sortKey="Janik, L J" uniqKey="Janik L">L.J. Janik</name></author><author><name sortKey="Viscarra Rossel, R A" uniqKey="Viscarra Rossel R">R.A. Viscarra Rossel</name></author><author><name sortKey="Macdonald, L M" uniqKey="Macdonald L">L.M. Macdonald</name></author><author><name sortKey="Mclaughlin, M J" uniqKey="Mclaughlin M">M.J. McLaughlin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stenberg, B" uniqKey="Stenberg B">B. Stenberg</name></author><author><name sortKey="Viscarra Rossel, R A" uniqKey="Viscarra Rossel R">R.A. Viscarra Rossel</name></author><author><name sortKey="Mouazen, A M" uniqKey="Mouazen A">A.M. Mouazen</name></author><author><name sortKey="Wetterlind, J" uniqKey="Wetterlind J">J. Wetterlind</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Schwartz, G" uniqKey="Schwartz G">G. Schwartz</name></author><author><name sortKey="Eshel, G" uniqKey="Eshel G">G. Eshel</name></author><author><name sortKey="Ben Dor, E" uniqKey="Ben Dor E">E. Ben-Dor</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shi, T" uniqKey="Shi T">T. Shi</name></author><author><name sortKey="Chen, Y" uniqKey="Chen Y">Y. Chen</name></author><author><name sortKey="Liu, Y" uniqKey="Liu Y">Y. Liu</name></author><author><name sortKey="Wu, G" uniqKey="Wu G">G. Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dennis, I A" uniqKey="Dennis I">I.A. Dennis</name></author><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author><author><name sortKey="Coulthard, T J" uniqKey="Coulthard T">T.J. Coulthard</name></author><author><name sortKey="Brewer, P A" uniqKey="Brewer P">P.A. Brewer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brewer, P A" uniqKey="Brewer P">P.A. Brewer</name></author><author><name sortKey="Dennis, I A" uniqKey="Dennis I">I.A. Dennis</name></author><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kooistra, L" uniqKey="Kooistra L">L. Kooistra</name></author><author><name sortKey="Wanders, J" uniqKey="Wanders J">J. Wanders</name></author><author><name sortKey="Epema, G F" uniqKey="Epema G">G.F. Epema</name></author><author><name sortKey="Leuven, R S E W" uniqKey="Leuven R">R.S.E.W. Leuven</name></author><author><name sortKey="Wehrens, R" uniqKey="Wehrens R">R. Wehrens</name></author><author><name sortKey="Buydens, L M C" uniqKey="Buydens L">L.M.C. Buydens</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Smith, K M" uniqKey="Smith K">K.M. Smith</name></author><author><name sortKey="Abrahams, P W" uniqKey="Abrahams P">P.W. Abrahams</name></author><author><name sortKey="Dagleish, M P" uniqKey="Dagleish M">M.P. Dagleish</name></author><author><name sortKey="Steigmajer, J" uniqKey="Steigmajer J">J. Steigmajer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ning, Y" uniqKey="Ning Y">Y. Ning</name></author><author><name sortKey="Li, J" uniqKey="Li J">J. Li</name></author><author><name sortKey="Cai, W" uniqKey="Cai W">W. Cai</name></author><author><name sortKey="Shao, X" uniqKey="Shao X">X. Shao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lamine, S" uniqKey="Lamine S">S. Lamine</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Evans, A" uniqKey="Evans A">A. Evans</name></author><author><name sortKey="Lamine, S" uniqKey="Lamine S">S. Lamine</name></author><author><name sortKey="Kalivas, D P" uniqKey="Kalivas D">D.P. Kalivas</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author><author><name sortKey="Ireland, G" uniqKey="Ireland G">G. Ireland</name></author><author><name sortKey="Lamine, S" uniqKey="Lamine S">S. Lamine</name></author><author><name sortKey="Griffiths, H M" uniqKey="Griffiths H">H.M. Griffiths</name></author><author><name sortKey="Ghilain, N" uniqKey="Ghilain N">N. Ghilain</name></author><author><name sortKey="Anagnostopoulos, V" uniqKey="Anagnostopoulos V">V. Anagnostopoulos</name></author><author><name sortKey="North, M R" uniqKey="North M">M.R. North</name></author><author><name sortKey="Srivastava, P K" uniqKey="Srivastava P">P.K. Srivastava</name></author><author><name sortKey="Georgopoulou, H" uniqKey="Georgopoulou H">H. Georgopoulou</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lamine, S" uniqKey="Lamine S">S. Lamine</name></author><author><name sortKey="Saunders, I" uniqKey="Saunders I">I. Saunders</name></author><author><name sortKey="Boukhalfa, S" uniqKey="Boukhalfa S">S. Boukhalfa</name></author><author><name sortKey="Petropoulos, G" uniqKey="Petropoulos G">G. Petropoulos</name></author><author><name sortKey="Bachari, N E I" uniqKey="Bachari N">N.E.I. Bachari</name></author><author><name sortKey="Brewer, P" uniqKey="Brewer P">P. Brewer</name></author><author><name sortKey="Macklin, M G" uniqKey="Macklin M">M.G. Macklin</name></author><author><name sortKey="Haresign, W" uniqKey="Haresign W">W. Haresign</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rodriguez Estival, J" uniqKey="Rodriguez Estival J">J. Rodríguez-Estival</name></author><author><name sortKey="Barasona, J A" uniqKey="Barasona J">J.A. Barasona</name></author><author><name sortKey="Mateo, R" uniqKey="Mateo R">R. Mateo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Neathery, M W" uniqKey="Neathery M">M.W. Neathery</name></author><author><name sortKey="Miller, W J" uniqKey="Miller W">W.J. Miller</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ward, N I" uniqKey="Ward N">N.I. Ward</name></author><author><name sortKey="Brooks, R R" uniqKey="Brooks R">R.R. Brooks</name></author><author><name sortKey="Roberts, E" uniqKey="Roberts E">E. Roberts</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Wolfenden, P J" uniqKey="Wolfenden P">P.J. Wolfenden</name></author><author><name sortKey="Lewin, J" uniqKey="Lewin J">J. Lewin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Clark, R N" uniqKey="Clark R">R.N. Clark</name></author><author><name sortKey="Roush, T L" uniqKey="Roush T">T.L. Roush</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Manevski, K" uniqKey="Manevski K">K. Manevski</name></author><author><name sortKey="Manakos, I" uniqKey="Manakos I">I. Manakos</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author><author><name sortKey="Kalaitzidis, C" uniqKey="Kalaitzidis C">C. Kalaitzidis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Manevski, K" uniqKey="Manevski K">K. Manevski</name></author><author><name sortKey="Manakos, I" uniqKey="Manakos I">I. Manakos</name></author><author><name sortKey="Petropoulos, G P" uniqKey="Petropoulos G">G.P. Petropoulos</name></author><author><name sortKey="Kalaitzidis, C" uniqKey="Kalaitzidis C">C. Kalaitzidis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Manevski, K" uniqKey="Manevski K">K. Manevski</name></author><author><name sortKey="Jabloun, M" uniqKey="Jabloun M">M. Jabloun</name></author><author><name sortKey="Gupta, M" uniqKey="Gupta M">M. Gupta</name></author><author><name sortKey="Kalaitzidis, C" uniqKey="Kalaitzidis C">C. Kalaitzidis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Der Meer, F" uniqKey="Van Der Meer F">F. Van der Meer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhao, K" uniqKey="Zhao K">K. Zhao</name></author><author><name sortKey="Valle, D" uniqKey="Valle D">D. Valle</name></author><author><name sortKey="Popescu, S" uniqKey="Popescu S">S. Popescu</name></author><author><name sortKey="Zhang, X" uniqKey="Zhang X">X. Zhang</name></author><author><name sortKey="Mallick, B" uniqKey="Mallick B">B. Mallick</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, K" uniqKey="Liu K">K. Liu</name></author><author><name sortKey="Zhao, D" uniqKey="Zhao D">D. Zhao</name></author><author><name sortKey="Fang, J Y" uniqKey="Fang J">J.-y. Fang</name></author><author><name sortKey="Zhang, X" uniqKey="Zhang X">X. Zhang</name></author><author><name sortKey="Zhang, Q Y" uniqKey="Zhang Q">Q.-y. Zhang</name></author><author><name sortKey="Li, X K" uniqKey="Li X">X.-k. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dong, J" uniqKey="Dong J">J. Dong</name></author><author><name sortKey="Dai, W" uniqKey="Dai W">W. Dai</name></author><author><name sortKey="Xu, J" uniqKey="Xu J">J. Xu</name></author><author><name sortKey="Li, S" uniqKey="Li S">S. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Siebielec, G" uniqKey="Siebielec G">G. Siebielec</name></author><author><name sortKey="Mccarty, G W" uniqKey="Mccarty G">G.W. McCarty</name></author><author><name sortKey="Stuczynski, T I" uniqKey="Stuczynski T">T.I. Stuczynski</name></author><author><name sortKey="Reeves, J B" uniqKey="Reeves J">J.B. Reeves</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mohamed, E S" uniqKey="Mohamed E">E.S. Mohamed</name></author><author><name sortKey="Saleh, A M" uniqKey="Saleh A">A.M. Saleh</name></author><author><name sortKey="Belal, A B" uniqKey="Belal A">A.B. Belal</name></author><author><name sortKey="Gad, A A" uniqKey="Gad A">A.A. Gad</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Leone, A P" uniqKey="Leone A">A.P. Leone</name></author><author><name sortKey="Sommer, S" uniqKey="Sommer S">S. Sommer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bachari, N E I" uniqKey="Bachari N">N.E.I. Bachari</name></author><author><name sortKey="Khodja, S" uniqKey="Khodja S">S. Khodja</name></author><author><name sortKey="Belbachir, A" uniqKey="Belbachir A">A. Belbachir</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id><journal-id journal-id-type="iso-abbrev">Sensors (Basel)</journal-id><journal-id journal-id-type="publisher-id">sensors</journal-id><journal-title-group><journal-title>Sensors (Basel, Switzerland)</journal-title></journal-title-group><issn pub-type="epub">1424-8220</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">30781812</article-id><article-id pub-id-type="pmc">6413008</article-id><article-id pub-id-type="doi">10.3390/s19040762</article-id><article-id pub-id-type="publisher-id">sensors-19-00762</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-0183-8820</contrib-id><name><surname>Lamine</surname><given-names>Salim</given-names></name><xref ref-type="aff" rid="af1-sensors-19-00762">1</xref><xref ref-type="aff" rid="af2-sensors-19-00762">2</xref><xref rid="c1-sensors-19-00762" ref-type="corresp">*</xref></contrib><contrib contrib-type="author"><name><surname>Petropoulos</surname><given-names>George P.</given-names></name><xref ref-type="aff" rid="af3-sensors-19-00762">3</xref><xref ref-type="aff" rid="af4-sensors-19-00762">4</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-0834-8848</contrib-id><name><surname>Brewer</surname><given-names>Paul A.</given-names></name><xref ref-type="aff" rid="af2-sensors-19-00762">2</xref></contrib><contrib contrib-type="author"><name><surname>Bachari</surname><given-names>Nour-El-Islam</given-names></name><xref ref-type="aff" rid="af5-sensors-19-00762">5</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-4155-630X</contrib-id><name><surname>Srivastava</surname><given-names>Prashant K.</given-names></name><xref ref-type="aff" rid="af6-sensors-19-00762">6</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-2068-3040</contrib-id><name><surname>Manevski</surname><given-names>Kiril</given-names></name><xref ref-type="aff" rid="af7-sensors-19-00762">7</xref></contrib><contrib contrib-type="author"><name><surname>Kalaitzidis</surname><given-names>Chariton</given-names></name><xref ref-type="aff" rid="af8-sensors-19-00762">8</xref></contrib><contrib contrib-type="author"><name><surname>Macklin</surname><given-names>Mark G.</given-names></name><xref ref-type="aff" rid="af9-sensors-19-00762">9</xref></contrib></contrib-group><aff id="af1-sensors-19-00762"><label>1</label>Faculty of Natural Sciences, Life and Earth Sciences, University Akli Mohand Oulhadj of Bouira, 10000 Bouira, Algeria</aff><aff id="af2-sensors-19-00762"><label>2</label>Department of Geography and Earth Sciences, University of Aberystwyth, Ceredigion, Wales SY23 3DB, UK;<email>pqb@aber.ac.uk</email></aff><aff id="af3-sensors-19-00762"><label>3</label>Department of Soil and Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1, Theofrastou St., 41335 Larisa, Greece;<email>petropoulos.george@gmail.com</email></aff><aff id="af4-sensors-19-00762"><label>4</label>School of Mineral and Resources Engineering, Technical University of Crete, Kounoupidiana Campus, 73100 Crete, Greece</aff><aff id="af5-sensors-19-00762"><label>5</label>Faculty of Biological Sciences, University of Sciences and Technology Houari Boumediene, BP 32, El Alia, Bab Ezzouar 16111, Algeria;<email>bacharinouri@gmail.com</email></aff><aff id="af6-sensors-19-00762"><label>6</label>Institute of Environment and Sustainable Development & DST Mahamana Center for Excellence in Climate Change Research, Banaras Hindu University, Varanasi 221005, India;<email>prashant.just@gmail.com</email></aff><aff id="af7-sensors-19-00762"><label>7</label>Department of Agroecology, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark;<email>kiril.manevski@agrsci.dk</email></aff><aff id="af8-sensors-19-00762"><label>8</label>Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania, 73100 Crete, Greece;<email>chariton@maich.gr</email></aff><aff id="af9-sensors-19-00762"><label>9</label>School of Geography, College of Science, University of Lincoln, Brayford Pool, Lincoln, Lincolnshire LN6 7TS, UK;<email>mmacklin@lincoln.ac.uk</email></aff><author-notes><corresp id="c1-sensors-19-00762"><label>*</label>Correspondence: <email>salim.lamine@gmail.com</email>; Tel.: +213-540-466-156</corresp></author-notes><pub-date pub-type="epub"><day>13</day><month>2</month><year>2019</year></pub-date><pub-date pub-type="collection"><month>2</month><year>2019</year></pub-date><volume>19</volume><issue>4</issue><elocation-id>762</elocation-id><history><date date-type="received"><day>23</day><month>11</month><year>2018</year></date><date date-type="accepted"><day>08</day><month>2</month><year>2019</year></date></history><permissions><copyright-statement>© 2019 by the authors.</copyright-statement><copyright-year>2019</copyright-year><license license-type="open-access"><license-p>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 (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>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<sup>®</sup> 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.</p></abstract><kwd-group><kwd>hyperspectral data</kwd><kwd>heavy metals</kwd><kwd>floodplain</kwd><kwd>soil spectral library</kwd><kwd>regression modelling</kwd></kwd-group></article-meta></front><body><sec sec-type="intro" id="sec1-sensors-19-00762"><title>1. Introduction</title><p>The United Kingdom (UK) Environment Agency has listed over 1300 former mining sites responsible for heavy metal contamination of both land and water [<xref rid="B1-sensors-19-00762" ref-type="bibr">1</xref>,<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>]. River systems can become contaminated by metals, for example lead (Pb), zinc (Zn), cadmium (Cd) and copper (Cu) if their drain catchments are underlain by mineralised geologies. In the UK, peak base-metal mining activity occurred in the 18th and the 19th centuries, when there was little or no environmental legislation preventing the release of contaminated water and sediments into the water courses. Floods are involved directly as serious agents of contaminant dispersion [<xref rid="B3-sensors-19-00762" ref-type="bibr">3</xref>,<xref rid="B4-sensors-19-00762" ref-type="bibr">4</xref>,<xref rid="B5-sensors-19-00762" ref-type="bibr">5</xref>], resulting in sedimentation on agricultural and residential lands, where contaminants may remain for 10s or 100s of years until they are remobilised via surface or river bank erosion. Contaminated floodplain soils and sediments pose a potential danger to human health, safety of agricultural products and may adversely affect the environment [<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>,<xref rid="B6-sensors-19-00762" ref-type="bibr">6</xref>]. </p><p>Considering the paramount importance of soil for food security and the increasing size of urbanisation, it is important to identify and manage metal contaminated sites [<xref rid="B7-sensors-19-00762" ref-type="bibr">7</xref>,<xref rid="B8-sensors-19-00762" ref-type="bibr">8</xref>,<xref rid="B9-sensors-19-00762" ref-type="bibr">9</xref>]. Therefore, an understanding of the contamination risk is required, as well as the development of quick, feasible and affordable estimation methods [<xref rid="B10-sensors-19-00762" ref-type="bibr">10</xref>,<xref rid="B11-sensors-19-00762" ref-type="bibr">11</xref>,<xref rid="B12-sensors-19-00762" ref-type="bibr">12</xref>,<xref rid="B13-sensors-19-00762" ref-type="bibr">13</xref>]. Traditional techniques for evaluating metals contamination in the environment typically involve field-based soil/sediment sampling, wet chemical digestion and subsequent laboratory analysis, followed by interpolating outputs to create spatial risk maps [<xref rid="B14-sensors-19-00762" ref-type="bibr">14</xref>,<xref rid="B15-sensors-19-00762" ref-type="bibr">15</xref>,<xref rid="B16-sensors-19-00762" ref-type="bibr">16</xref>]. However, such approaches are time-consuming and often very expensive [<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>,<xref rid="B17-sensors-19-00762" ref-type="bibr">17</xref>,<xref rid="B18-sensors-19-00762" ref-type="bibr">18</xref>,<xref rid="B19-sensors-19-00762" ref-type="bibr">19</xref>]. Advances in hyperspectral remote sensing are increasingly being applied in metal soil contamination studies, providing a more rapid, cost-effective and spatially extensive way to map contamination [<xref rid="B20-sensors-19-00762" ref-type="bibr">20</xref>,<xref rid="B21-sensors-19-00762" ref-type="bibr">21</xref>,<xref rid="B22-sensors-19-00762" ref-type="bibr">22</xref>,<xref rid="B23-sensors-19-00762" ref-type="bibr">23</xref>,<xref rid="B24-sensors-19-00762" ref-type="bibr">24</xref>].</p><p>The utility of hyperspectral imaging to map the distribution of heavy metals in mining regions has previously been demonstrated by several studies [<xref rid="B25-sensors-19-00762" ref-type="bibr">25</xref>,<xref rid="B26-sensors-19-00762" ref-type="bibr">26</xref>,<xref rid="B27-sensors-19-00762" ref-type="bibr">27</xref>,<xref rid="B28-sensors-19-00762" ref-type="bibr">28</xref>,<xref rid="B29-sensors-19-00762" ref-type="bibr">29</xref>,<xref rid="B30-sensors-19-00762" ref-type="bibr">30</xref>]. Soil properties and concentration of minerals can be determined using hyperspectral imaging techniques since these are able to provide spectrally-rich and spatially-continuous information that can be extended for mapping and monitoring of soil contamination. Reflectance spectroradiometry is relatively more cost-effective than traditional measurements based on chemistry [<xref rid="B17-sensors-19-00762" ref-type="bibr">17</xref>,<xref rid="B31-sensors-19-00762" ref-type="bibr">31</xref>,<xref rid="B32-sensors-19-00762" ref-type="bibr">32</xref>,<xref rid="B33-sensors-19-00762" ref-type="bibr">33</xref>,<xref rid="B34-sensors-19-00762" ref-type="bibr">34</xref>]. </p><p>Spectral signatures obtained from soil constituents are distinguished based on their reflectance in specific bands of the electromagnetic spectrum [<xref rid="B35-sensors-19-00762" ref-type="bibr">35</xref>,<xref rid="B36-sensors-19-00762" ref-type="bibr">36</xref>,<xref rid="B37-sensors-19-00762" ref-type="bibr">37</xref>]. Visible (VIS, 350–800 nm), near infrared (NIR, 800–1350 nm) and shortwave infrared (SWIR, 1350–2500 nm) spectroradiometers are used largely in soil science, since they can be handled easily in the field. After correct calibration, they can be used to estimate several soil properties such as total carbon and nitrogen, sand and clay contents, cation exchange capacity and pH (e.g., [<xref rid="B38-sensors-19-00762" ref-type="bibr">38</xref>,<xref rid="B39-sensors-19-00762" ref-type="bibr">39</xref>]). Schwartz et al. [<xref rid="B40-sensors-19-00762" ref-type="bibr">40</xref>] summarises the application of VNIR reflectance for estimating the soil contamination, and Shi et al. [<xref rid="B41-sensors-19-00762" ref-type="bibr">41</xref>] review the role of VNIR soil spectra for predicting concentration of heavy metals.</p><p>This study aims at investigating the potential added value of field spectroradiometry when combined with geochemical analyses of Pb, Zn, Cu and Cd, to quantify and model heavy metal soil contamination (HMSC). The specific objectives are to: (i) collect field- and lab-based spectra from contaminated soils and build associated spectral libraries; (ii) identify the specific spectral intervals associated with the modelling of HMSC by performing statistical discrimination analyses; (iii) collect and geochemically analyse the soil samples; and (iv) develop and validate a heavy metal prediction model (HMPM) using soil metal concentration and spectral reflectance data. The study explores, for the first time, the potential of spectrally discriminating contaminant metals in floodplain soils, which has significant implications for the mapping and management of contaminated soils in mining-affected river catchments. The main research hypotheses were that: (i) soil spectra exhibit differences in specific wavelengths, which support their spectral discrimination; (ii) heavy metal concentrations can be retrieved from the spectra at high accuracy; and (iii) the samples with the highest heavy metal concentrations (high concentration of heavy metals means the colour of soil will be darker) would have the lowest reflectance (or the highest absorbance) and that reflectance would increase proportionally as heavy metal concentrations decreased.</p></sec><sec id="sec2-sensors-19-00762"><title>2. Widespread Dispersal and Hazards of Heavy Metals in the UK </title><p>Even though metal mining activity ceased almost a century ago, many west-draining rivers influenced by the flooding of June 2012 registered high concentrations of heavy metals in flood sediments that exceeded national and European standards [<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>]. Macklin et al. [<xref rid="B4-sensors-19-00762" ref-type="bibr">4</xref>], Dennis et al. [<xref rid="B42-sensors-19-00762" ref-type="bibr">42</xref>] and Brewer et al. [<xref rid="B43-sensors-19-00762" ref-type="bibr">43</xref>] pointed out that at the catchments where historical metal mining took place, massive floods can cause dispersion, overbank sedimentation of highly contaminated constituents. In particular, deposition of fine-grained metal contaminated sediment on floodplains can pose a serious potential risk to the vigour, organisation and resilience of ecosystem services. Previous studies in the Ystwyth valley brought to light that sheep has the capacity to ingest high concentrations of heavy metals per day (1685 mg of Pb, 486 mg of Zn and 60 mg Cu), especially from the green vegetation during the winter [<xref rid="B44-sensors-19-00762" ref-type="bibr">44</xref>,<xref rid="B45-sensors-19-00762" ref-type="bibr">45</xref>,<xref rid="B46-sensors-19-00762" ref-type="bibr">46</xref>].</p><p>The extraction of Pb, Zn and Cu from West Wales has a long history, linked back to the Roman period or the Bronze Age in some regions. Generally, Pb and Zn mining peaked in the mid-19th century, with most mining operations closed by the beginning of the 20th century [<xref rid="B22-sensors-19-00762" ref-type="bibr">22</xref>,<xref rid="B47-sensors-19-00762" ref-type="bibr">47</xref>,<xref rid="B48-sensors-19-00762" ref-type="bibr">48</xref>,<xref rid="B49-sensors-19-00762" ref-type="bibr">49</xref>,<xref rid="B50-sensors-19-00762" ref-type="bibr">50</xref>].</p><p>Many European researches have reported that offal can hold an elevated concentration of metals. Rodríguez-Estival et al. [<xref rid="B51-sensors-19-00762" ref-type="bibr">51</xref>] unexpectedly discovered that 91.4% of cattle and 13.5% of sheep had high blood Pb concentrations related to a subclinical vulnerability, and two cattle had blood Pb concentrations expressive of clinical poisoning. The previous studies are related directly with results from West Wales and presented the detailed risks of floodplain contamination from bovine species, which are found to be very vulnerable to Pb poisoning, especially young animals [<xref rid="B52-sensors-19-00762" ref-type="bibr">52</xref>,<xref rid="B53-sensors-19-00762" ref-type="bibr">53</xref>]. Furthermore, when the produced meat of the poisoned animals reaches the food chain, human health will be in danger. The problem of soil contamination by heavy metals in West Wales, and of course many other areas of the UK, will increase as a result of floods that happened in the past century. Therefore, innovative monitoring techniques, such as hyperspectral remote sensing, are highly recommended to characterise qualitatively and quantitatively the heavy metal contamination and investigate the short-term solutions, to protect the ecosystem services at large and human health specifically [<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>,<xref rid="B28-sensors-19-00762" ref-type="bibr">28</xref>].</p></sec><sec id="sec3-sensors-19-00762"><title>3. Materials and Methods</title><sec id="sec3dot1-sensors-19-00762"><title>3.1. Study Area and Soil Sampling</title><p>The study area was Bow Street in West Wales, the UK. The site comprises a triangular plot of about 40 ha of land, divided into a series of experimental plots managed by Aberystwyth University’s Institute of Biological, Environmental and Rural Sciences (IBERS). The main land cover of the study area is forage crops that are used for grazing of sheep or cattle (<xref ref-type="fig" rid="sensors-19-00762-f001">Figure 1</xref>).</p><p>A total of 85 surface soil samples (0–5 cm) were collected from the study site using a stainless-steel trowel (<xref ref-type="fig" rid="sensors-19-00762-f001">Figure 1</xref>). After removing the vegetation from the soil surface, each soil sample of about 500 g was an aggregation of five individual spot samples obtained from a 1 m<sup>2</sup> area. Samples were placed in wet-strength soil bags and, in the laboratory, they were oven-dried for 48 h at 40 °C. The reference for the soil samples locations was the work performed by Foulds et al. [<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>], who studied the contaminated area and found that flood sediments were polluted at a higher level of contamination guidelines. More importantly, crop silage harvested from the flood affected region was found to cumulate up to 1900 mg·kg<sup>−1</sup> of sediment-associated Pb, which caused cattle poisoning and mortality. Two years later, in 2014, authors joined the research group of Paul Brewer and Mark Macklin at Aberystwyth University and have planned together to build on their work by testing the hyperspectral imaging method that has not been used before on this polluted site.</p></sec><sec id="sec3dot2-sensors-19-00762"><title>3.2. Field and Laboratory Spectral Measurements</title><p>Field spectra were acquired during August 2014 using ASD (Analytical Spectral Devices) FieldSpec<sup>®</sup> 3 portable spectroradiometer in a hand-held mode. The instrument has a spectral resolution of 3 nm in the 350–1000 nm range and 10 nm in the 1001–2500 nm range, both ranges interpolated to 1 nm during the measurements. The field spectral measurements took place before taking the soil samples from the 85 locations. After removing the surface vegetation, five spectral measurements were performed directly from each sample location and averaged to a single representative spectrum. Later on, the soil samples were collected following the exact spots (each location has 5 spots) used during the spectral measurements. In the field, the sun was the only source of illumination and the measurements were taken from 10 AM to 2 PM under clear sky conditions. White panel reference data were recorded before each soil measurement. Before starting the measurements, a warm up time of 30 min was given to minimise errors caused by the warming of the spectroradiometer array. To collect the spectra, a pistol grip was pointed towards the soil at 50 cm height. The radius of the field-of-view (FOV) was 3.5 cm, as estimated using Equation (1), where R is the radius of the FOV, H is the height from the soil to the sensor in the pistol grip and AOV is the angle-of-view of the sensor (8°).
<disp-formula>R = tg(AOV/2) × H × 100 [cm]<label>(1)</label></disp-formula></p><p>In the laboratory, the collection of soil spectra was performed with a high-intensity contact probe (CP; direct contact with the soil). The ASD CP setup has a 100 W reflectorised halogen lamp aligned at 12° to the probe body, and the sensed spot has a diameter d<sub>probe</sub> = 1.1 cm with a FOV = 1.33 cm<sup>2</sup>. Analogous to the field spectra, before starting the measurement, a warm up time of 30 min was respected. To collect the soil spectrum, soil (particle size < 2 mm) was placed in a black plastic dish (size of Petri dish) and the ASD CP was put in a direct contact with the soil, followed by registration of the spectrum. Each measurement was repeated three times and averaged to representative spectrum per sample (<xref ref-type="fig" rid="sensors-19-00762-f002">Figure 2</xref> and <xref ref-type="fig" rid="sensors-19-00762-f003">Figure 3</xref>).</p></sec><sec id="sec3dot3-sensors-19-00762"><title>3.3. Geochemistry Analysis of the Soil Samples</title><p>The extraction of heavy metals (Cd, Cu, Pb and Zn) was performed using nitric acid. A number of acids and acid mixtures (e.g., hydrofluoric-perchloric-nitric or perchloric-nitric) are efficient in decomposing samples of rock, soils or sediments. Concentrated nitric acid alone has a less vigorous effect than mixed acid decomposition methods and, in particular, iron (Fe (III)) oxide minerals are not attacked strongly. However, the following procedure is an efficient method for metals such as Cd, Cu, Pb and Zn. Regarding the geochemistry analysis, the soil samples were put in the oven to dry at 40 °C. Soil samples with small particle size (< 63 μm) were examined in the laboratory, as this fraction category usually shows the highest concentration with metals [<xref rid="B55-sensors-19-00762" ref-type="bibr">55</xref>]. </p><p>For the 85 soil samples, the geochemistry analysis followed these steps: (1) weigh 0.5 ± 0.005 g of soil using a weighing boat and transfer each sample into a clearly labelled boiling tube; (2) use of an automatic dispenser to add 2 ml of concentrated nitric acid carefully to the sample; (3) place the test tube carefully into the digestion block that has been exposed to 100 °C and leave it for 1 h, (4) take the boiling tube out of the block and leave to cool; (5) use an automatic dispenser to add 18 ml of distilled water to the contents of the tube and mix thoroughly with a whirlimixer (Fisher Scientific Ltd., Loughborough, UK); and (6) leave overnight (covered in cling film) to allow any suspended particles to settle. After that, the samples can be carefully sprayed into the flame of an atomic absorption spectrometer (PerkinElmer Inc., Shelton, CT, USA) without blocking the capillary tube. The dilution factor for this method is 40, but in cases where the concentration was higher than the calibration range of the spectrometer, the samples were diluted using an automatic dilutor by preparing serial dilutions of X 10. For the aim to control the analytical methods applied in the current study, certified reference material (GBW 07307 stream sediment) was made ready and analysed following identical steps as used for HMSC.</p></sec><sec id="sec3dot4-sensors-19-00762"><title>3.4. Data Processing and Statistics</title><p>The obtained field and lab spectra were continuum-removed and normalized to increase the spectral absorption features. The continuum-removal analysis suggested by Clark and Roush [<xref rid="B56-sensors-19-00762" ref-type="bibr">56</xref>] is the standard transformation in land cover spectral discrimination [<xref rid="B57-sensors-19-00762" ref-type="bibr">57</xref>,<xref rid="B58-sensors-19-00762" ref-type="bibr">58</xref>,<xref rid="B59-sensors-19-00762" ref-type="bibr">59</xref>]. The continuum is a convex hull of straight-line segments, fitted over a spectrum and subsequently removed by division or rationing relative to the spectrum [<xref rid="B60-sensors-19-00762" ref-type="bibr">60</xref>].</p><p>Since not all wavelengths are adequate for detection of heavy metals in soils, analysis of variance (ANOVA) was conducted first to find out broader spectral bands sensitive to heavy metal concentrations. This analysis was at each wavelength from 350 to 2500 nm for each spectral library (field- and lab-based) at 95% confidence level. ANOVA was followed by correlation analysis between the spectral features and heavy metal concentrations following the Pearson’s correlation coefficient. The latter give an excellent estimation of dependence between two quantities, and it is calculated after dividing the covariance of the two variables by the product of their standard deviation. The correlation coefficient can be estimated by Equation (2), where E is the expected value, <italic>μ</italic> is the mean, <italic>cov</italic> is the covariance between x and y and <italic>corr</italic> is the correlation coefficient [<xref rid="B37-sensors-19-00762" ref-type="bibr">37</xref>].
<disp-formula id="FD1-sensors-19-00762"><label>(2)</label><mml:math id="mm1"><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>X</mml:mi><mml:mo>,</mml:mo><mml:mi>Y</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>v</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>X</mml:mi><mml:mo>,</mml:mo><mml:mi>Y</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>σ</mml:mi><mml:mi>X</mml:mi><mml:mi>σ</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant="normal">E</mml:mi><mml:mo stretchy="false">[</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant="normal">X</mml:mi><mml:mo>−</mml:mo><mml:mi>μ</mml:mi><mml:mi mathvariant="normal">x</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant="normal">Y</mml:mi><mml:mo>−</mml:mo><mml:mi>μ</mml:mi><mml:mi mathvariant="normal">y</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>σ</mml:mi><mml:mi>X</mml:mi><mml:mi>σ</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>High correlation coefficient between the independent variable (spectral features) and the dependent variable (heavy metal concentrations) indicates a strong linear correlation. The correlation was conducted at 95% confidence level. This was followed by stepwise multiple linear regression (SMLR) to create the HMSC models. For the SMLR, at each step the independent variable (wavelengths) not in the equation and with the smallest probability of F is entered, provided that the probability is sufficiently small. Variables in the regression equation are removed if their probability of F becomes sufficiently large. The method stops when no more variables are eligible for inclusion or removal [<xref rid="B61-sensors-19-00762" ref-type="bibr">61</xref>]. The generated regression models were written following Equation (3): <disp-formula>HMSC (mg kg<sup>−1</sup>) = [A<sub>n</sub><italic>R</italic><sub>350–2500</sub> + B] × 1000<label>(3)</label></disp-formula>where HMSC is the heavy metal soil concentration (mg kg<sup>−1</sup>), A<sub>n</sub> is the slope of the regression (n coefficients of the regression), <italic>R</italic><sub>350–2500</sub> is the reflectance wavelength varying from 350 to 2500 nm, B is the regression constant and we multiply the result by 1000 in order to obtain the concentration of heavy metal with mg per kg (mg kg<sup>−1</sup>). The overall methodology framework is depicted on <xref ref-type="fig" rid="sensors-19-00762-f003">Figure 3</xref>.</p></sec></sec><sec sec-type="results" id="sec4-sensors-19-00762"><title>4. Results and Discussion</title><sec id="sec4dot1-sensors-19-00762"><title>4.1. Soil Descriptive Statistics</title><p>Pb, Zn, Cu and Cd are the major heavy metals in the study area [<xref rid="B2-sensors-19-00762" ref-type="bibr">2</xref>], thus they were selected as potential contaminants. The descriptive statistics of the geochemistry analyses are summed up in <xref rid="sensors-19-00762-t001" ref-type="table">Table 1</xref>. The standard deviation (Stdev) of Pb and Zn was very high (1037.96 and 59.85, respectively), indicating large spatial variability of these two metals and existence of “hot spots” (i.e., localized small areas with their very high concentration; <xref ref-type="fig" rid="sensors-19-00762-f003">Figure 3</xref> and <xref ref-type="fig" rid="sensors-19-00762-f004">Figure 4</xref>). The concentrations of Cu and especially Cd showed lower magnitude and variation. As Pb was found in the highest concentration compared to the other heavy metals, its concentration might be significantly responsible for the variation of the soil reflectance. </p><p><xref ref-type="fig" rid="sensors-19-00762-f004">Figure 4</xref> below tends to support the third hypothesis by showing lower reflectance of the highly contaminated soil sample (sample 57) compared to the reflectance of the low contaminated soil sample (sample 73), although more studies are needed to further support this. Herein, it can be clearly noted that the reflectance was governed by the concentration of heavy metals in each sample. For sample 57, which had the maximum concentration of the four heavy metals, the reflectance was lower than sample 73, which had the minimum concentration (<xref ref-type="fig" rid="sensors-19-00762-f005">Figure 5</xref>).</p></sec><sec id="sec4dot2-sensors-19-00762"><title>4.2. Development of Field- and Lab-Based Spectral Libraries</title><p>The field- and the lab-based soil spectral libraries, composed of 85 spectra each, are shown on <xref ref-type="fig" rid="sensors-19-00762-f006">Figure 6</xref> and <xref ref-type="fig" rid="sensors-19-00762-f007">Figure 7</xref>, respectively. Overall, the soil spectra resembled the typical shape of an increase in the VIS and relatively constant in the NIR and SWIR, with some local dips around 1400, 1900 and 2200 nm due to water and clay absorption. Yet, major variation in the soil reflectance for both libraries could be seen in the NIR and especially SWIR, likely due to variation in soil properties such as moisture, clay and organic matter content, but also due to content of heavy metals. The two spectral libraries enrich the spectra database and may serve as “reference” spectra for heavy metal contaminated soils in the UK, and; thus, authorise appropriate validation of the reflectance information extracted from radiance data acquired from remote platforms, and can play an important role in tracking temporal changes of the soil spectra over the sampling locations. </p></sec><sec id="sec4dot3-sensors-19-00762"><title>4.3. Statistical Discrimination Analysis </title><p>The results of the ANOVA on the continuum-removed reflectance at each wavelength, from 350–2500 nm, are plotted in <xref ref-type="fig" rid="sensors-19-00762-f008">Figure 8</xref> and <xref ref-type="fig" rid="sensors-19-00762-f009">Figure 9</xref>, for the field- and the lab-based spectral libraries, respectively. The results are based on the continuum removed spectra of the soils and the red-dashed line on the figures denote the critical P value (0.05), below which statistically significant results are achieved (shaded grey on the figures, i.e., spectral regions that contain at least one significantly different soil spectral from the others).</p><p>The results showed that there were statistically significant differences in the mean continuum-removed field-based soil spectra along most of the VIS spectrum, from 350 to 800 nm, and narrower windows in the NIR and SWIR. For the lab-based soil spectra, the majority of the statistically significant results could be seen in the VNIR, from 360 to 1270 nm, with few narrow windows at the end of the NIR. Yet, ANOVA cannot reduce the number of wavelengths to those most influential for discriminating between the soils, but can serve as an input for further statistical modelling of HMSC.</p></sec><sec id="sec4dot4-sensors-19-00762"><title>4.4. Model Development and Validation</title><p>The coefficient of determination (R<sup>2</sup>, the square of the Pearson correlation coefficient) for the SMLR models shows information about useful spectral bands that were mostly used in building of the models; the selected wavebands used to build the field-based and lab-based prediction models are listed in <xref rid="sensors-19-00762-t002" ref-type="table">Table 2</xref> and <xref rid="sensors-19-00762-t003" ref-type="table">Table 3</xref>, respectively. In the regression analyses, the spectral bands were considered as an independent variable, whereas the concentration of heave metals were implicated as a dependent variable. For every single step in the SMLR, the non-included independent variable with the lowest probability is integrated, only if its probability is small. Finally, the performance quality for each calibration model was evaluated by R<sup>2</sup>. Song et al. [<xref rid="B37-sensors-19-00762" ref-type="bibr">37</xref>] built models to assess aluminium, copper and chrome in the soil and water of a mining area in China. These authors derived, from measured spectra, spectral features characteristic for their metals. More importantly, they also found notable linear correlation between spectral wavebands and heavy metal concentrations. Therefore, the bands spectra selected from field- and lab-reflectance spectra are involved in the construction of the prediction models. Liu et al. [<xref rid="B62-sensors-19-00762" ref-type="bibr">62</xref>] investigated Cd and Pb concentrations spectroradiometrically in Chinese soils and, based on SMLR, achieved R<sup>2</sup> for estimating the heavy metal contents of about 0.65–0.82 for Cd and 0.78 to 0.88 for Pb. The R<sup>2</sup> values reported in our study were somewhat similar and sometimes better than previous studies; the predictive power of the models may be tested and possibly improved by using unaltered or transformed spectra (e.g., logarithm of reciprocal spectra; [<xref rid="B62-sensors-19-00762" ref-type="bibr">62</xref>]; derivative spectra; [<xref rid="B39-sensors-19-00762" ref-type="bibr">39</xref>]) and other statistical models (e.g., generalized regression neural network; [<xref rid="B63-sensors-19-00762" ref-type="bibr">63</xref>]).</p><p>Based on <xref rid="sensors-19-00762-t002" ref-type="table">Table 2</xref>, the four developed field-based HMPMs were:<disp-formula>Pb<sub>FSpec</sub> = [−320.758<italic>R</italic><sub>354</sub> + 456.742<italic>R</italic><sub>389</sub> − 94.144<italic>R</italic><sub>582</sub> + 92.316<italic>R</italic><sub>1719</sub> − 82.081<italic>R</italic><sub>1775</sub> − 0.172] × 1000</disp-formula><disp-formula>Zn<sub>FSpec</sub> = [−64.043<italic>R</italic><sub>366</sub> + 71.865<italic>R</italic><sub>374</sub> − 57.897<italic>R</italic><sub>386</sub> + 90.868<italic>R</italic><sub>388</sub> + 66.374<italic>R</italic><sub>393</sub> − 96.782<italic>R</italic><sub>394</sub> − 6.142<italic>R</italic><sub>586</sub> + 0.965<italic>R</italic><sub>1348</sub> + 0.139] × 1000</disp-formula><disp-formula>Cu<sub>FSpec</sub> = [−81.125<italic>R</italic><sub>367</sub> + 42.275<italic>R</italic><sub>368</sub> + 64.551<italic>R</italic><sub>389</sub> − 23.652<italic>R</italic><sub>434</sub> + 0.026] × 1000</disp-formula><disp-formula>Cd<sub>FSpec</sub> = [0.008<italic>R</italic><sub>1951</sub> − 0.007<italic>R</italic><sub>1978</sub> + 0.001] × 1000</disp-formula></p><p>Based on <xref rid="sensors-19-00762-t003" ref-type="table">Table 3</xref>, the four developed lab-based HMPMs were:<disp-formula>Pb<sub>LSpec</sub> = (90.729<italic>R</italic><sub>356</sub> − 25.105 <italic>R</italic><sub>618</sub> − 0.057) × 1000</disp-formula><disp-formula>Zn<sub>LSpec</sub> = (−4.369 <italic>R</italic><sub>358</sub> + 5.055 <italic>R<sub>368</sub></italic> + 9.101 <italic>R</italic><sub>376</sub> − 78.747 <italic>R</italic><sub>470</sub> + 127.870 <italic>R</italic><sub>475</sub> − 53.910 <italic>R</italic><sub>484</sub> − 0.048) × 1000</disp-formula><disp-formula>Cu<sub>LSpec</sub> = (2.502<italic>R</italic><sub>359</sub> − 0.628<italic>R</italic><sub>651</sub> − 0.016) × 1000</disp-formula><disp-formula>Cd<sub>LSpec</sub> = (−0.001 <italic>R</italic><sub>1465</sub> + 0.002) × 1000</disp-formula></p><p>Many of the wavelengths correlated with the soil heavy metal concentrations were in the VIS bands, probably due to molecules in the soil responsible for some traits characterising this region, such as organic matter content and its various chemical functional groups. Reflectance (i.e., absorbance) bands are generally caused by fundamental molecular vibrations; yet, most heavy metals do not present specific spectral features in the NIR and SWIR, so understanding of the physical relationship between the spectral data and the heavy metals is not straightforward and is probably influenced by presence/absence of other inorganic components, such as iron cations and phosphate and carbonate anions [<xref rid="B64-sensors-19-00762" ref-type="bibr">64</xref>]. It is; therefore, primarily the mathematical relations (e.g., HMPM) that can be used for prediction, testing and calibration/validation purposes [<xref rid="B64-sensors-19-00762" ref-type="bibr">64</xref>,<xref rid="B65-sensors-19-00762" ref-type="bibr">65</xref>].</p><p>The ASD field spectroradiometric data are non-destructive and efficient for estimation of heavy metal levels in the soil. High correlation between the estimated heavy metal concentrations and the predicted heavy metal contents highlights the feasibility of using SMLR to build reliable predictive models with spectral measurements and geochemical variables from laboratory analyses [<xref rid="B62-sensors-19-00762" ref-type="bibr">62</xref>,<xref rid="B66-sensors-19-00762" ref-type="bibr">66</xref>,<xref rid="B67-sensors-19-00762" ref-type="bibr">67</xref>]. Furthermore, there is no need for chemicals reagents, and it requires minimal sample preparation. The present research contributes to the growing field of hyperspectral imaging by advancing the knowledge on how a combination of field spectroradiometric data with geochemical analyses can be used to predict heavy metal contamination and to assess better the environmental quality.</p></sec></sec><sec sec-type="conclusions" id="sec5-sensors-19-00762"><title>5. Conclusions</title><p>The present research clearly highlighted the potential role of combining field and laboratory hyperspectral data with geochemical data of Pb, Zn, Cu and Cd in order to quantify and model heavy metal contamination by considering a highly contaminated floodplain site from Wales in the west of the UK. The results confirm the pre-defined study hypotheses: first, that soil spectral signatures exhibit differences in specific wavelengths of the spectrum, thus supporting their spectral discrimination; second, that heavy metal concentrations can be retrieved from spectral reflectance data at reasonable accuracy, using field spectroradiometer covering the spectral range of 350–2500 nm; and third, that the samples with the highest heavy metal concentrations would have the lowest reflectance and that reflectance would increase as heavy metal concentrations decreased. </p><p>Herein, the field- and lab-based spectral features derived from 85 soil samples of the contaminated area were used successfully to develop two spectral libraries, which have been combined to build eight heavy metal prediction models using SMLR. The findings demonstrate high feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and spectroradiometry. </p><p>The problem of heavy metal contamination in West Wales and several other areas in the UK can be characterised effectively using hyperspectral spectroradiometry, which has the potential as a rapid, low cost technique for mapping HMSC. However, as the spectral features of soil in the spectral range from 350–2500 nm is very complex, finding exact spectral wavebands attributable to Pb, Zn, Cu and Cd concentrations, which are unaffected by the chemical composition and physical conditions at the soil surface, is a significant challenge. The developed prediction models provide an alternative tool for predicting the heavy metal contamination by using field and laboratory hyperspectral measurements. The produced models can be a basis for mapping heavy metal concentrations over a large area by using space-borne hyperspectral sensors such as Hyperion, AVIRIS, EnMAP and CHRIS Proba.</p></sec></body><back><ack><title>Acknowledgments</title><p>Authors would like to thank William Haresign (Emeritus Professor at the Institute of Biological, Environmental and Rural Sciences, Animal Systems Research Group, Aberystwyth University) for facilitating the access to the study site and his advice in the field protocol. The authors appreciate the effort of the laboratory engineer Wynne Ebenezer assisting the geochemistry analyses. Authors also thank the anonymous reviewers for their constructive and very useful comments.</p></ack><notes><title>Author Contributions</title><p>Conceptualization, S.L., G.P.P., P.A.B. and M.G.M.; Methodology, S.L., G.P.P., K.M. and P.A.B.; Software, S.L.; Validation, S.L., G.P.P., P.A.B., N.-E.-I.B., P.K.S., K.M., C.K., M.G.M.; Formal Analysis, S.L.; Investigation, S.L.; Resources, S.L., G.P.P., P.A.B., N.-E.-I.B., P.K.S., K.M., C.K., M.G.M.; Data Curation, S.L., G.P.P., P.A.B., N.-E.-I.B., P.K.S., K.M., C.K., M.G.M.; Writing-Original Draft Preparation, S.L., G.P.P., K.M., P.A.B., N.-E.-I.B., P.K.S., C.K. and M.G.M.; Writing-Review & Editing, S.L., G.P.P., K.M. and P.A.B.; Visualization, S.L., G.P.P., P.A.B.; Supervision, S.L., G.P.P., P.A.B., N.-E.-I.B., P.K.S., K.M., C.K., M.G.M.; Project Administration, S.L., G.P.P., P.A.B., N.-E.-I.B.; Funding Acquisition, S.L. and G.P.P.</p></notes><notes><title>Funding</title><p>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.</p></notes><notes notes-type="COI-statement"><title>Conflicts of Interest</title><p>The authors declare no conflict of interest.</p></notes><ref-list><title>References</title><ref id="B1-sensors-19-00762"><label>1.</label><element-citation publication-type="confproc"><person-group person-group-type="author"><name><surname>Johnston</surname><given-names>D.</given-names></name></person-group><article-title>A Metal Mines Strategy for Wales</article-title><source>Proceedings of the International Mine Water Association Symposium</source><conf-loc>Newcastle upon Tyne, UK</conf-loc><conf-date>20–25 September 2004</conf-date></element-citation></ref><ref id="B2-sensors-19-00762"><label>2.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Foulds</surname><given-names>S.A.</given-names></name><name><surname>Brewer</surname><given-names>P.A.</given-names></name><name><surname>Macklin</surname><given-names>M.G.</given-names></name><name><surname>Haresign</surname><given-names>W.</given-names></name><name><surname>Betson</surname><given-names>R.E.</given-names></name><name><surname>Rassner</surname><given-names>S.M.E.</given-names></name></person-group><article-title>Flood-related contamination in catchments affected by historical metal mining: An unexpected and emerging hazard of climate change</article-title><source>Sci. Total Environ.</source><year>2014</year><volume>476</volume><fpage>165</fpage><lpage>180</lpage><pub-id pub-id-type="doi">10.1016/j.scitotenv.2013.12.079</pub-id><pub-id pub-id-type="pmid">24463253</pub-id></element-citation></ref><ref id="B3-sensors-19-00762"><label>3.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Macklin</surname><given-names>M.G.</given-names></name><name><surname>Hudson-Edwards</surname><given-names>K.A.</given-names></name><name><surname>Dawson</surname><given-names>E.J.</given-names></name></person-group><article-title>The significance of pollution from historic metal mining in the Pennine orefields on river sediment contaminant fluxes to the North Sea</article-title><source>Sci. Total Environ.</source><year>1997</year><volume>194</volume><fpage>391</fpage><lpage>397</lpage><pub-id pub-id-type="doi">10.1016/S0048-9697(96)05378-8</pub-id></element-citation></ref><ref id="B4-sensors-19-00762"><label>4.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Macklin</surname><given-names>M.G.</given-names></name><name><surname>Brewer</surname><given-names>P.A.</given-names></name><name><surname>Hudson-Edwards</surname><given-names>K.A.</given-names></name><name><surname>Bird</surname><given-names>G.</given-names></name><name><surname>Coulthard</surname><given-names>T.J.</given-names></name><name><surname>Dennis</surname><given-names>I.A.</given-names></name><name><surname>Lechler</surname><given-names>P.J.</given-names></name><name><surname>Miller</surname><given-names>J.R.</given-names></name><name><surname>Turner</surname><given-names>J.N.</given-names></name></person-group><article-title>A geomorphological approach to the management of rivers contaminated by metal mining</article-title><source>Geomorphology</source><year>2006</year><volume>79</volume><fpage>423</fpage><lpage>447</lpage><pub-id pub-id-type="doi">10.1016/j.geomorph.2006.06.024</pub-id></element-citation></ref><ref id="B5-sensors-19-00762"><label>5.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mayes</surname><given-names>W.M.</given-names></name><name><surname>Potter</surname><given-names>H.A.B.</given-names></name><name><surname>Jarvis</surname><given-names>A.P.</given-names></name></person-group><article-title>Riverine flux of metals from historically mined orefields in England and Wales</article-title><source>Water Air Soil Pollut.</source><year>2013</year><volume>224</volume><fpage>1425</fpage><pub-id pub-id-type="doi">10.1007/s11270-012-1425-9</pub-id></element-citation></ref><ref id="B6-sensors-19-00762"><label>6.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gozzard</surname><given-names>E.</given-names></name><name><surname>Mayes</surname><given-names>W.M.</given-names></name><name><surname>Potter</surname><given-names>H.A.B.</given-names></name><name><surname>Jarvis</surname><given-names>A.P.</given-names></name></person-group><article-title>Seasonal and spatial variation of diffuse (non-point) source zinc pollution in a historically metal mined river catchment, UK</article-title><source>Environ. Pollut.</source><year>2011</year><volume>159</volume><fpage>3113</fpage><lpage>3122</lpage><pub-id pub-id-type="doi">10.1016/j.envpol.2011.02.010</pub-id><pub-id pub-id-type="pmid">21561697</pub-id></element-citation></ref><ref id="B7-sensors-19-00762"><label>7.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Henke</surname><given-names>J.M.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name></person-group><article-title>A GIS-based exploration of the relationships between human health, social deprivation and ecosystem services: The case of Wales, UK</article-title><source>Appl. Geogr.</source><year>2013</year><volume>45</volume><fpage>77</fpage><lpage>88</lpage><pub-id pub-id-type="doi">10.1016/j.apgeog.2013.07.022</pub-id></element-citation></ref><ref id="B8-sensors-19-00762"><label>8.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Liu</surname><given-names>Y.</given-names></name><name><surname>Wen</surname><given-names>C.</given-names></name><name><surname>Liu</surname><given-names>X.</given-names></name></person-group><article-title>China’s food security soiled by contamination</article-title><source>Science</source><year>2013</year><volume>339</volume><fpage>1382</fpage><lpage>1383</lpage><pub-id pub-id-type="doi">10.1126/science.339.6126.1382-b</pub-id><pub-id pub-id-type="pmid">23520093</pub-id></element-citation></ref><ref id="B9-sensors-19-00762"><label>9.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Luo</surname><given-names>X.-S.</given-names></name><name><surname>Yu</surname><given-names>S.</given-names></name><name><surname>Zhu</surname><given-names>Y.-G.</given-names></name><name><surname>Li</surname><given-names>X.-D.</given-names></name></person-group><article-title>Trace metal contamination in urban soils of china</article-title><source>Sci. Total Environ.</source><year>2012</year><volume>421</volume><fpage>17</fpage><lpage>30</lpage><pub-id pub-id-type="doi">10.1016/j.scitotenv.2011.04.020</pub-id><pub-id pub-id-type="pmid">21575982</pub-id></element-citation></ref><ref id="B10-sensors-19-00762"><label>10.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Choe</surname><given-names>E.</given-names></name><name><surname>Kim</surname><given-names>K.-W.</given-names></name><name><surname>Bang</surname><given-names>S.</given-names></name><name><surname>Yoon</surname><given-names>I.-H.</given-names></name><name><surname>Lee</surname><given-names>K.-Y.</given-names></name></person-group><article-title>Qualitative analysis and mapping of heavy metals in an abandoned Au–Ag mine area using NIR spectroscopy</article-title><source>Environ. Geol.</source><year>2008</year><volume>58</volume><fpage>477</fpage><lpage>482</lpage><pub-id pub-id-type="doi">10.1007/s00254-008-1520-9</pub-id></element-citation></ref><ref id="B11-sensors-19-00762"><label>11.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cai</surname><given-names>Q.-Y.</given-names></name><name><surname>Mo</surname><given-names>C.-H.</given-names></name><name><surname>Li</surname><given-names>H.-Q.</given-names></name><name><surname>Lü</surname><given-names>H.</given-names></name><name><surname>Zeng</surname><given-names>Q.-Y.</given-names></name><name><surname>Li</surname><given-names>Y.-W.</given-names></name><name><surname>Wu</surname><given-names>X.-L.</given-names></name></person-group><article-title>Heavy metal contamination of urban soils and dusts in Guangzhou, South China</article-title><source>Environ. Monit. Assess.</source><year>2012</year><volume>185</volume><fpage>1095</fpage><lpage>1106</lpage><pub-id pub-id-type="doi">10.1007/s10661-012-2617-x</pub-id><pub-id pub-id-type="pmid">22592780</pub-id></element-citation></ref><ref id="B12-sensors-19-00762"><label>12.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Al Maliki</surname><given-names>A.</given-names></name><name><surname>Bruce</surname><given-names>D.</given-names></name><name><surname>Owens</surname><given-names>G.</given-names></name></person-group><article-title>Prediction of lead concentration in soil using reflectance spectroscopy</article-title><source>Environ. Technol. Innov.</source><year>2014</year><volume>1</volume><fpage>8</fpage><lpage>15</lpage><pub-id pub-id-type="doi">10.1016/j.eti.2014.08.002</pub-id></element-citation></ref><ref id="B13-sensors-19-00762"><label>13.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pandit</surname><given-names>C.M.</given-names></name><name><surname>Filippelli</surname><given-names>G.M.</given-names></name><name><surname>Li</surname><given-names>L.</given-names></name></person-group><article-title>Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression</article-title><source>Int. J. Remote Sens.</source><year>2010</year><volume>31</volume><fpage>4111</fpage><lpage>4123</lpage><pub-id pub-id-type="doi">10.1080/01431160903229200</pub-id></element-citation></ref><ref id="B14-sensors-19-00762"><label>14.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Gupta</surname><given-names>M.</given-names></name><name><surname>Mukherjee</surname><given-names>S.</given-names></name></person-group><article-title>Mapping spatial distribution of pollutants in groundwater of a tropical area of India using remote sensing and GIS</article-title><source>Appl. Geomatics</source><year>2011</year><volume>4</volume><fpage>21</fpage><lpage>32</lpage><pub-id pub-id-type="doi">10.1007/s12518-011-0072-y</pub-id></element-citation></ref><ref id="B15-sensors-19-00762"><label>15.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Han</surname><given-names>D.</given-names></name><name><surname>Gupta</surname><given-names>M.</given-names></name><name><surname>Mukherjee</surname><given-names>S.</given-names></name></person-group><article-title>Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis</article-title><source>Hydrol. Sci. J.</source><year>2012</year><volume>57</volume><fpage>1453</fpage><lpage>1472</lpage><pub-id pub-id-type="doi">10.1080/02626667.2012.716156</pub-id></element-citation></ref><ref id="B16-sensors-19-00762"><label>16.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sharma</surname><given-names>N.K.</given-names></name><name><surname>Bhardwaj</surname><given-names>S.</given-names></name><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Thanki</surname><given-names>Y.J.</given-names></name><name><surname>Gadhia</surname><given-names>P.K.</given-names></name><name><surname>Gadhia</surname><given-names>M.</given-names></name></person-group><article-title>Soil chemical changes resulting from irrigating with petrochemical effluents</article-title><source>Int. J. Environ. Sci. Technol.</source><year>2012</year><volume>9</volume><fpage>361</fpage><lpage>370</lpage><pub-id pub-id-type="doi">10.1007/s13762-012-0039-5</pub-id></element-citation></ref><ref id="B17-sensors-19-00762"><label>17.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Choe</surname><given-names>E.</given-names></name><name><surname>van der Meer</surname><given-names>F.</given-names></name><name><surname>van Ruitenbeek</surname><given-names>F.</given-names></name><name><surname>van der Werff</surname><given-names>H.</given-names></name><name><surname>de Smeth</surname><given-names>B.</given-names></name><name><surname>Kim</surname><given-names>K.-W.</given-names></name></person-group><article-title>Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain</article-title><source>Remote Sens. Environ.</source><year>2008</year><volume>112</volume><fpage>3222</fpage><lpage>3233</lpage><pub-id pub-id-type="doi">10.1016/j.rse.2008.03.017</pub-id></element-citation></ref><ref id="B18-sensors-19-00762"><label>18.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Djokić</surname><given-names>B.V.</given-names></name><name><surname>Jović</surname><given-names>V.</given-names></name><name><surname>Jovanović</surname><given-names>M.</given-names></name><name><surname>Ćirić</surname><given-names>A.</given-names></name><name><surname>Jovanović</surname><given-names>D.</given-names></name></person-group><article-title>Geochemical behaviour of some heavy metals of the Grot flotation tailing, Southeast Serbia</article-title><source>Environ. Earth Sci.</source><year>2011</year><volume>66</volume><fpage>933</fpage><lpage>939</lpage><pub-id pub-id-type="doi">10.1007/s12665-011-1303-6</pub-id></element-citation></ref><ref id="B19-sensors-19-00762"><label>19.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname><given-names>B.</given-names></name><name><surname>Wu</surname><given-names>D.</given-names></name><name><surname>Zhang</surname><given-names>L.</given-names></name><name><surname>Jiao</surname><given-names>Q.</given-names></name><name><surname>Li</surname><given-names>Q.</given-names></name></person-group><article-title>Application of hyperspectral remote sensing for environment monitoring in mining areas</article-title><source>Environ. Earth Sci.</source><year>2011</year><volume>65</volume><fpage>649</fpage><lpage>658</lpage><pub-id pub-id-type="doi">10.1007/s12665-011-1112-y</pub-id></element-citation></ref><ref id="B20-sensors-19-00762"><label>20.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Farrand</surname><given-names>W.H.</given-names></name><name><surname>Harsanyi</surname><given-names>J.C.</given-names></name></person-group><article-title>Mapping the distribution of mine tailings in the Coeur d’Alene River Valley, Idaho, through the use of a constrained energy minimization technique</article-title><source>Remote Sens. Environ.</source><year>1997</year><volume>59</volume><fpage>64</fpage><lpage>76</lpage><pub-id pub-id-type="doi">10.1016/S0034-4257(96)00080-6</pub-id></element-citation></ref><ref id="B21-sensors-19-00762"><label>21.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ferrier</surname><given-names>G.</given-names></name></person-group><article-title>Application of imaging spectrometer data in identifying environmental pollution caused by mining at Rodaquilar, Spain</article-title><source>Remote Sens. Environ.</source><year>1999</year><volume>68</volume><fpage>125</fpage><lpage>137</lpage><pub-id pub-id-type="doi">10.1016/S0034-4257(98)00105-9</pub-id></element-citation></ref><ref id="B22-sensors-19-00762"><label>22.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lamine</surname><given-names>S.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name><name><surname>Singh</surname><given-names>S.K.</given-names></name><name><surname>Szabó</surname><given-names>S.</given-names></name><name><surname>Bachari</surname><given-names>N.E.I.</given-names></name><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Suman</surname><given-names>S.</given-names></name></person-group><article-title>Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS<sup>®</sup></article-title><source>Geocarto Int.</source><year>2018</year><volume>33</volume><fpage>862</fpage><lpage>878</lpage><pub-id pub-id-type="doi">10.1080/10106049.2017.1307460</pub-id></element-citation></ref><ref id="B23-sensors-19-00762"><label>23.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>El Islam</surname><given-names>B.N.</given-names></name><name><surname>Fouzia</surname><given-names>H.</given-names></name><name><surname>Khalid</surname><given-names>A.</given-names></name></person-group><article-title>Combination of satellite images and numerical model for the state followed the coast of the bay of Bejaia-Jijel</article-title><source>Int. J. Environ. Geoinf.</source><year>2017</year><volume>4</volume><fpage>1</fpage><lpage>7</lpage><pub-id pub-id-type="doi">10.30897/ijegeo.306486</pub-id></element-citation></ref><ref id="B24-sensors-19-00762"><label>24.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Meharrar</surname><given-names>K.</given-names></name><name><surname>Bachari</surname><given-names>N.E.I.</given-names></name></person-group><article-title>Modelling of radiative transfer of natural surfaces in the solar radiation spectrum: Development of a satellite data simulator (SDDS)</article-title><source>Int. J. Remote Sens.</source><year>2014</year><volume>35</volume><fpage>1199</fpage><lpage>1216</lpage><pub-id pub-id-type="doi">10.1080/01431161.2013.876116</pub-id></element-citation></ref><ref id="B25-sensors-19-00762"><label>25.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Liu</surname><given-names>M.</given-names></name><name><surname>Liu</surname><given-names>X.</given-names></name><name><surname>Li</surname><given-names>J.</given-names></name><name><surname>Li</surname><given-names>T.</given-names></name></person-group><article-title>Estimating regional heavy metal concentrations in rice by scaling up a field-scale heavy metal assessment model</article-title><source>Int. J. Appl. Earth Obs. Geoinf.</source><year>2012</year><volume>19</volume><fpage>12</fpage><lpage>23</lpage><pub-id pub-id-type="doi">10.1016/j.jag.2012.04.014</pub-id></element-citation></ref><ref id="B26-sensors-19-00762"><label>26.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>You</surname><given-names>D.</given-names></name><name><surname>Zhou</surname><given-names>J.</given-names></name><name><surname>Wang</surname><given-names>J.</given-names></name><name><surname>Ma</surname><given-names>Z.</given-names></name><name><surname>Pan</surname><given-names>L.</given-names></name></person-group><article-title>Analysis of relations of heavy metal accumulation with land utilization using the positive and negative association rule method</article-title><source>Math. Comput. Model.</source><year>2011</year><volume>54</volume><fpage>1005</fpage><lpage>1009</lpage><pub-id pub-id-type="doi">10.1016/j.mcm.2010.11.028</pub-id></element-citation></ref><ref id="B27-sensors-19-00762"><label>27.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Singh</surname><given-names>S.K.</given-names></name><name><surname>Gupta</surname><given-names>M.</given-names></name><name><surname>Thakur</surname><given-names>J.K.</given-names></name><name><surname>Mukherjee</surname><given-names>S.</given-names></name></person-group><article-title>Modeling impact of land use change trajectories on groundwater quality using remote sensing and GIS</article-title><source>Environ. Eng. Manag. J.</source><year>2013</year><volume>12</volume><fpage>2343</fpage><lpage>2355</lpage><pub-id pub-id-type="doi">10.30638/eemj.2013.287</pub-id></element-citation></ref><ref id="B28-sensors-19-00762"><label>28.</label><element-citation publication-type="confproc"><person-group person-group-type="author"><name><surname>Lamine</surname><given-names>S.</given-names></name><name><surname>Brewer</surname><given-names>P.A.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name><name><surname>Kalaitzidis</surname><given-names>C.</given-names></name><name><surname>Manevski</surname><given-names>K.</given-names></name><name><surname>Macklin</surname><given-names>M.G.</given-names></name><name><surname>Haresign</surname><given-names>W.</given-names></name></person-group><article-title>Investigating the potential of hyperspectral imaging (HSI) for the quantitative estimation of lead contamination in soil (LCS)</article-title><source>Proceedings of the HSI 2014—Hyperspectral Imaging and Applications</source><conf-loc>Coventry, UK</conf-loc><conf-date>15–16 October 2014</conf-date></element-citation></ref><ref id="B29-sensors-19-00762"><label>29.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Pandley</surname><given-names>P.C.</given-names></name><name><surname>Manevski</surname><given-names>K.</given-names></name><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name></person-group><article-title>The Use of Hyperspectral Earth observation Data for Land Use/Cover Classification: Present Status, Challenges and Future Outlook</article-title><source>Hyperspectral Remote Sensing of Vegetation</source><edition>1st ed.</edition><person-group person-group-type="editor"><name><surname>Thenkabail</surname><given-names>P.</given-names></name></person-group><publisher-name>Taylor & Francis CRC Press</publisher-name><publisher-loc>London, UK</publisher-loc><year>2018</year><fpage>147</fpage><lpage>173</lpage></element-citation></ref><ref id="B30-sensors-19-00762"><label>30.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rosero-Vlasova</surname><given-names>O.A.</given-names></name><name><surname>Pérez-Cabello</surname><given-names>F.</given-names></name><name><surname>Montorio Llovería</surname><given-names>R.</given-names></name><name><surname>Vlassova</surname><given-names>L.</given-names></name></person-group><article-title>Assessment of laboratory VIS-NIR-SWIR setups with different spectroscopy accessories for characterisation of soils from wildfire burns</article-title><source>Biosyst. Eng.</source><year>2016</year><volume>152</volume><fpage>51</fpage><lpage>67</lpage><pub-id pub-id-type="doi">10.1016/j.biosystemseng.2016.06.011</pub-id></element-citation></ref><ref id="B31-sensors-19-00762"><label>31.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Summers</surname><given-names>D.</given-names></name></person-group><article-title>Discriminating and mapping soil variability with hyperspectral reflectance data</article-title><source>Ph.D. Thesis</source><publisher-name>Adelaide University</publisher-name><publisher-loc>Adelaide, Australia</publisher-loc><year>2009</year></element-citation></ref><ref id="B32-sensors-19-00762"><label>32.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ben-Dor</surname><given-names>E.</given-names></name><name><surname>Patkin</surname><given-names>K.</given-names></name><name><surname>Banin</surname><given-names>A.</given-names></name><name><surname>Karnieli</surname><given-names>A.</given-names></name></person-group><article-title>Mapping of several soil properties using DAIS-7915 hyperspectral scanner data—A case study over clayey soils in Israel</article-title><source>Int. J. Remote Sens.</source><year>2002</year><volume>23</volume><fpage>1043</fpage><lpage>1062</lpage><pub-id pub-id-type="doi">10.1080/01431160010006962</pub-id></element-citation></ref><ref id="B33-sensors-19-00762"><label>33.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname><given-names>Y.</given-names></name><name><surname>Chen</surname><given-names>J.</given-names></name><name><surname>Wu</surname><given-names>X.</given-names></name><name><surname>Tian</surname><given-names>Q.</given-names></name><name><surname>Ji</surname><given-names>J.</given-names></name><name><surname>Qin</surname><given-names>Z.</given-names></name></person-group><article-title>Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils</article-title><source>Appl. Geochem.</source><year>2005</year><volume>20</volume><fpage>1051</fpage><lpage>1059</lpage><pub-id pub-id-type="doi">10.1016/j.apgeochem.2005.01.009</pub-id></element-citation></ref><ref id="B34-sensors-19-00762"><label>34.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ren</surname><given-names>H.-Y.</given-names></name><name><surname>Zhuang</surname><given-names>D.-F.</given-names></name><name><surname>Singh</surname><given-names>A.N.</given-names></name><name><surname>Pan</surname><given-names>J.-J.</given-names></name><name><surname>Qiu</surname><given-names>D.-S.</given-names></name><name><surname>Shi</surname><given-names>R.-H.</given-names></name></person-group><article-title>Estimation of as and cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study</article-title><source>Pedosphere</source><year>2009</year><volume>19</volume><fpage>719</fpage><lpage>726</lpage><pub-id pub-id-type="doi">10.1016/S1002-0160(09)60167-3</pub-id></element-citation></ref><ref id="B35-sensors-19-00762"><label>35.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Horta</surname><given-names>A.</given-names></name><name><surname>Malone</surname><given-names>B.</given-names></name><name><surname>Stockmann</surname><given-names>U.</given-names></name><name><surname>Minasny</surname><given-names>B.</given-names></name><name><surname>Bishop</surname><given-names>T.F.A.</given-names></name><name><surname>McBratney</surname><given-names>A.B.</given-names></name><name><surname>Pallasser</surname><given-names>R.</given-names></name><name><surname>Pozza</surname><given-names>L.</given-names></name></person-group><article-title>Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review</article-title><source>Geoderma</source><year>2015</year><volume>241</volume><fpage>180</fpage><lpage>209</lpage><pub-id pub-id-type="doi">10.1016/j.geoderma.2014.11.024</pub-id></element-citation></ref><ref id="B36-sensors-19-00762"><label>36.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Nocita</surname><given-names>M.</given-names></name><name><surname>Stevens</surname><given-names>A.</given-names></name><name><surname>van Wesemael</surname><given-names>B.</given-names></name><name><surname>Aitkenhead</surname><given-names>M.</given-names></name><name><surname>Bachmann</surname><given-names>M.</given-names></name><name><surname>Barthès</surname><given-names>B.</given-names></name><name><surname>Ben Dor</surname><given-names>E.</given-names></name><name><surname>Brown</surname><given-names>D.J.</given-names></name><name><surname>Clairotte</surname><given-names>M.</given-names></name><name><surname>Csorba</surname><given-names>A.</given-names></name><etal></etal></person-group><article-title>Soil spectroscopy: An alternative to wet chemistry for soil monitoring</article-title><source>Advances in Agronomy</source><publisher-name>Elsevier B.V.</publisher-name><publisher-loc>Amsterdam, The Netherlands</publisher-loc><year>2015</year><fpage>139</fpage><lpage>159</lpage></element-citation></ref><ref id="B37-sensors-19-00762"><label>37.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Song</surname><given-names>L.</given-names></name><name><surname>Jian</surname><given-names>J.</given-names></name><name><surname>Tan</surname><given-names>D.-J.</given-names></name><name><surname>Xie</surname><given-names>H.-B.</given-names></name><name><surname>Luo</surname><given-names>Z.-F.</given-names></name><name><surname>Gao</surname><given-names>B.</given-names></name></person-group><article-title>Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area, Chongqing, China</article-title><source>Int. J. Appl. Earth Obs. Geoinf.</source><year>2015</year><volume>34</volume><fpage>1</fpage><lpage>9</lpage><pub-id pub-id-type="doi">10.1016/j.jag.2014.06.013</pub-id></element-citation></ref><ref id="B38-sensors-19-00762"><label>38.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Soriano-Disla</surname><given-names>J.M.</given-names></name><name><surname>Janik</surname><given-names>L.J.</given-names></name><name><surname>Viscarra Rossel</surname><given-names>R.A.</given-names></name><name><surname>Macdonald</surname><given-names>L.M.</given-names></name><name><surname>McLaughlin</surname><given-names>M.J.</given-names></name></person-group><article-title>The performance of visible, near- and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties</article-title><source>Appl. Spectrosc. Rev.</source><year>2013</year><volume>49</volume><fpage>139</fpage><lpage>186</lpage><pub-id pub-id-type="doi">10.1080/05704928.2013.811081</pub-id></element-citation></ref><ref id="B39-sensors-19-00762"><label>39.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Stenberg</surname><given-names>B.</given-names></name><name><surname>Viscarra Rossel</surname><given-names>R.A.</given-names></name><name><surname>Mouazen</surname><given-names>A.M.</given-names></name><name><surname>Wetterlind</surname><given-names>J.</given-names></name></person-group><article-title>Visible and near infrared spectroscopy in soil science</article-title><source>Advances in Agronomy</source><publisher-name>Elsevier B.V.</publisher-name><publisher-loc>Amsterdam, The Netherlands</publisher-loc><year>2010</year><fpage>163</fpage><lpage>215</lpage></element-citation></ref><ref id="B40-sensors-19-00762"><label>40.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Schwartz</surname><given-names>G.</given-names></name><name><surname>Eshel</surname><given-names>G.</given-names></name><name><surname>Ben-Dor</surname><given-names>E.</given-names></name></person-group><article-title>Reflectance spectroscopy as a tool for monitoring contaminated soils</article-title><source>Soil Contamination</source><publisher-name>InTech</publisher-name><publisher-loc>London, UK</publisher-loc><year>2011</year></element-citation></ref><ref id="B41-sensors-19-00762"><label>41.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Shi</surname><given-names>T.</given-names></name><name><surname>Chen</surname><given-names>Y.</given-names></name><name><surname>Liu</surname><given-names>Y.</given-names></name><name><surname>Wu</surname><given-names>G.</given-names></name></person-group><article-title>Visible and near-infrared reflectance spectroscopy—An alternative for monitoring soil contamination by heavy metals</article-title><source>J. Hazard. Mater.</source><year>2014</year><volume>265</volume><fpage>166</fpage><lpage>176</lpage><pub-id pub-id-type="doi">10.1016/j.jhazmat.2013.11.059</pub-id><pub-id pub-id-type="pmid">24361494</pub-id></element-citation></ref><ref id="B42-sensors-19-00762"><label>42.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dennis</surname><given-names>I.A.</given-names></name><name><surname>Macklin</surname><given-names>M.G.</given-names></name><name><surname>Coulthard</surname><given-names>T.J.</given-names></name><name><surname>Brewer</surname><given-names>P.A.</given-names></name></person-group><article-title>The impact of the October-November 2000 floods on contaminant metal dispersal in the River Swale catchment, North Yorkshire, UK</article-title><source>Hydrol. Processes</source><year>2003</year><volume>17</volume><fpage>1641</fpage><lpage>1657</lpage><pub-id pub-id-type="doi">10.1002/hyp.1206</pub-id></element-citation></ref><ref id="B43-sensors-19-00762"><label>43.</label><element-citation publication-type="gov"><person-group person-group-type="author"><name><surname>Brewer</surname><given-names>P.A.</given-names></name><name><surname>Dennis</surname><given-names>I.A.</given-names></name><name><surname>Macklin</surname><given-names>M.G.</given-names></name></person-group><article-title>The use of geomorphological mapping and modelling for identifying land affected by metal contamination on river floodplains. DEFRA project code. SP 0525</article-title><comment>Available online: <ext-link ext-link-type="uri" xlink:href="http://sciencesearch.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&ProjectID=10969">http://sciencesearch.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&ProjectID=10969</ext-link></comment><date-in-citation content-type="access-date" iso-8601-date="2019-02-12">(accessed on 12 February 2019)</date-in-citation></element-citation></ref><ref id="B44-sensors-19-00762"><label>44.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kooistra</surname><given-names>L.</given-names></name><name><surname>Wanders</surname><given-names>J.</given-names></name><name><surname>Epema</surname><given-names>G.F.</given-names></name><name><surname>Leuven</surname><given-names>R.S.E.W.</given-names></name><name><surname>Wehrens</surname><given-names>R.</given-names></name><name><surname>Buydens</surname><given-names>L.M.C.</given-names></name></person-group><article-title>The potential of field spectroscopy for the assessment of sediment properties in river floodplains</article-title><source>Anal. Chim. Acta</source><year>2003</year><volume>484</volume><fpage>189</fpage><lpage>200</lpage><pub-id pub-id-type="doi">10.1016/S0003-2670(03)00331-3</pub-id></element-citation></ref><ref id="B45-sensors-19-00762"><label>45.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Smith</surname><given-names>K.M.</given-names></name><name><surname>Abrahams</surname><given-names>P.W.</given-names></name><name><surname>Dagleish</surname><given-names>M.P.</given-names></name><name><surname>Steigmajer</surname><given-names>J.</given-names></name></person-group><article-title>The intake of lead and associated metals by sheep grazing mining-contaminated floodplain pastures in Mid-Wales, UK: I. Soil ingestion, soil–metal partitioning and potential availability to pasture herbage and livestock</article-title><source>Sci. Total Environ.</source><year>2009</year><volume>407</volume><fpage>3731</fpage><lpage>3739</lpage><pub-id pub-id-type="doi">10.1016/j.scitotenv.2009.02.032</pub-id><pub-id pub-id-type="pmid">19327816</pub-id></element-citation></ref><ref id="B46-sensors-19-00762"><label>46.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ning</surname><given-names>Y.</given-names></name><name><surname>Li</surname><given-names>J.</given-names></name><name><surname>Cai</surname><given-names>W.</given-names></name><name><surname>Shao</surname><given-names>X.</given-names></name></person-group><article-title>Simultaneous determination of heavy metal ions in water using near-infrared spectroscopy with preconcentration by nano-hydroxyapatite</article-title><source>Spectrochim. Acta Part A</source><year>2012</year><volume>96</volume><fpage>289</fpage><lpage>294</lpage><pub-id pub-id-type="doi">10.1016/j.saa.2012.05.034</pub-id></element-citation></ref><ref id="B47-sensors-19-00762"><label>47.</label><element-citation publication-type="confproc"><person-group person-group-type="author"><name><surname>Lamine</surname><given-names>S.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name></person-group><article-title>Evaluation of the Spectral Angle Mapper “SAM” Classification Technique using Hyperion Imagery</article-title><source>Proceedings of the European Space Agency Living Planet Symposium</source><conf-loc>Edinburgh, UK</conf-loc><conf-date>9–13 September 2013</conf-date></element-citation></ref><ref id="B48-sensors-19-00762"><label>48.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Evans</surname><given-names>A.</given-names></name><name><surname>Lamine</surname><given-names>S.</given-names></name><name><surname>Kalivas</surname><given-names>D.P.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name></person-group><article-title>Exploring the potential of EO data and GIS for ecosystem health modeling in response to wildfire: A case study in central Greece</article-title><source>Environ. Eng. Manag. J.</source><year>2018</year><volume>17</volume><fpage>9</fpage></element-citation></ref><ref id="B49-sensors-19-00762"><label>49.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name><name><surname>Ireland</surname><given-names>G.</given-names></name><name><surname>Lamine</surname><given-names>S.</given-names></name><name><surname>Griffiths</surname><given-names>H.M.</given-names></name><name><surname>Ghilain</surname><given-names>N.</given-names></name><name><surname>Anagnostopoulos</surname><given-names>V.</given-names></name><name><surname>North</surname><given-names>M.R.</given-names></name><name><surname>Srivastava</surname><given-names>P.K.</given-names></name><name><surname>Georgopoulou</surname><given-names>H.</given-names></name></person-group><article-title>Operational evapotranspiration estimates from seviri in support of sustainable water management</article-title><source>Int. J. Appl. Earth Obs. Geoinf.</source><year>2016</year><volume>49</volume><fpage>175</fpage><lpage>187</lpage><pub-id pub-id-type="doi">10.1016/j.jag.2016.02.006</pub-id></element-citation></ref><ref id="B50-sensors-19-00762"><label>50.</label><element-citation publication-type="confproc"><person-group person-group-type="author"><name><surname>Lamine</surname><given-names>S.</given-names></name><name><surname>Saunders</surname><given-names>I.</given-names></name><name><surname>Boukhalfa</surname><given-names>S.</given-names></name><name><surname>Petropoulos</surname><given-names>G.</given-names></name><name><surname>Bachari</surname><given-names>N.E.I.</given-names></name><name><surname>Brewer</surname><given-names>P.</given-names></name><name><surname>Macklin</surname><given-names>M.G.</given-names></name><name><surname>Haresign</surname><given-names>W.</given-names></name></person-group><article-title>Phytoremediation of heavy metals–contaminated soils by two willow species Salix viminalis and Salix dasyclados</article-title><source>Proceedings of the Seminar International Environnement, Agriculture et Biotechnologie (SIEAB)</source><conf-loc>Bouira, Algeria</conf-loc><conf-date>27–28 November 2017</conf-date></element-citation></ref><ref id="B51-sensors-19-00762"><label>51.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rodríguez-Estival</surname><given-names>J.</given-names></name><name><surname>Barasona</surname><given-names>J.A.</given-names></name><name><surname>Mateo</surname><given-names>R.</given-names></name></person-group><article-title>Blood Pb and δ-ALAD inhibition in cattle and sheep from a Pb-polluted mining area</article-title><source>Environ. Pollut.</source><year>2012</year><volume>160</volume><fpage>118</fpage><lpage>124</lpage><pub-id pub-id-type="doi">10.1016/j.envpol.2011.09.031</pub-id><pub-id pub-id-type="pmid">22035934</pub-id></element-citation></ref><ref id="B52-sensors-19-00762"><label>52.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Neathery</surname><given-names>M.W.</given-names></name><name><surname>Miller</surname><given-names>W.J.</given-names></name></person-group><article-title>Metabolism and toxicity of cadmium, mercury, and lead in animals: A review</article-title><source>J. Dairy Sci.</source><year>1975</year><volume>58</volume><fpage>1767</fpage><lpage>1781</lpage><pub-id pub-id-type="doi">10.3168/jds.S0022-0302(75)84785-0</pub-id><pub-id pub-id-type="pmid">1107364</pub-id></element-citation></ref><ref id="B53-sensors-19-00762"><label>53.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ward</surname><given-names>N.I.</given-names></name><name><surname>Brooks</surname><given-names>R.R.</given-names></name><name><surname>Roberts</surname><given-names>E.</given-names></name></person-group><article-title>Lead levels in sheep organs resulting from pollution from automotive exhausts</article-title><source>Environ. Pollut.</source><year>1978</year><volume>17</volume><fpage>7</fpage><lpage>12</lpage><pub-id pub-id-type="doi">10.1016/0013-9327(78)90050-2</pub-id></element-citation></ref><ref id="B54-sensors-19-00762"><label>54.</label><element-citation publication-type="book"><person-group person-group-type="author"><collab>ASD</collab></person-group><source>Integrating sphere user manual, ASD document 600660</source><publisher-name>ASD Inc.</publisher-name><publisher-loc>Boulder, CO, USA</publisher-loc><year>2008</year></element-citation></ref><ref id="B55-sensors-19-00762"><label>55.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wolfenden</surname><given-names>P.J.</given-names></name><name><surname>Lewin</surname><given-names>J.</given-names></name></person-group><article-title>Distribution of metal pollutants in floodplain sediments</article-title><source>Catena</source><year>1977</year><volume>4</volume><fpage>309</fpage><lpage>317</lpage><pub-id pub-id-type="doi">10.1016/0341-8162(77)90030-3</pub-id></element-citation></ref><ref id="B56-sensors-19-00762"><label>56.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Clark</surname><given-names>R.N.</given-names></name><name><surname>Roush</surname><given-names>T.L.</given-names></name></person-group><article-title>Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications</article-title><source>J. Geophys. Res. Solid Earth</source><year>1984</year><volume>89</volume><fpage>6329</fpage><lpage>6340</lpage><pub-id pub-id-type="doi">10.1029/JB089iB07p06329</pub-id></element-citation></ref><ref id="B57-sensors-19-00762"><label>57.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Manevski</surname><given-names>K.</given-names></name><name><surname>Manakos</surname><given-names>I.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name><name><surname>Kalaitzidis</surname><given-names>C.</given-names></name></person-group><article-title>Discrimination of common mediterranean plant species using field spectroradiometry</article-title><source>Int. J. Appl. Earth Obs. Geoinf.</source><year>2011</year><volume>13</volume><fpage>922</fpage><lpage>933</lpage><pub-id pub-id-type="doi">10.1016/j.jag.2011.07.001</pub-id></element-citation></ref><ref id="B58-sensors-19-00762"><label>58.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Manevski</surname><given-names>K.</given-names></name><name><surname>Manakos</surname><given-names>I.</given-names></name><name><surname>Petropoulos</surname><given-names>G.P.</given-names></name><name><surname>Kalaitzidis</surname><given-names>C.</given-names></name></person-group><article-title>Spectral discrimination of Mediterranean Maquis and Phrygana vegetation: Results from a case study in Greece</article-title><source>IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.</source><year>2012</year><volume>5</volume><fpage>604</fpage><lpage>616</lpage><pub-id pub-id-type="doi">10.1109/JSTARS.2012.2190044</pub-id></element-citation></ref><ref id="B59-sensors-19-00762"><label>59.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Manevski</surname><given-names>K.</given-names></name><name><surname>Jabloun</surname><given-names>M.</given-names></name><name><surname>Gupta</surname><given-names>M.</given-names></name><name><surname>Kalaitzidis</surname><given-names>C.</given-names></name></person-group><article-title>Field-scale sensitivity of vegetation discrimination to hyperspectral reflectance and coupled statistics</article-title><source>Sensitivity Analysis in Earth Observation Modelling</source><publisher-name>Elsevier B.V.</publisher-name><publisher-loc>Amsterdam, The Netherlands</publisher-loc><year>2017</year><fpage>103</fpage><lpage>121</lpage></element-citation></ref><ref id="B60-sensors-19-00762"><label>60.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Van der Meer</surname><given-names>F.</given-names></name></person-group><article-title>Indicator kriging applied to absorption band analysis in hyperspectral imagery: A case study from the Rodalquilar epithermal gold mining area, SE Spain</article-title><source>Int. J. Appl. Earth Obs. Geoinf.</source><year>2006</year><volume>8</volume><fpage>61</fpage><lpage>72</lpage><pub-id pub-id-type="doi">10.1016/j.jag.2005.07.001</pub-id></element-citation></ref><ref id="B61-sensors-19-00762"><label>61.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zhao</surname><given-names>K.</given-names></name><name><surname>Valle</surname><given-names>D.</given-names></name><name><surname>Popescu</surname><given-names>S.</given-names></name><name><surname>Zhang</surname><given-names>X.</given-names></name><name><surname>Mallick</surname><given-names>B.</given-names></name></person-group><article-title>Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection</article-title><source>Remote Sens. Environ.</source><year>2013</year><volume>132</volume><fpage>102</fpage><lpage>119</lpage><pub-id pub-id-type="doi">10.1016/j.rse.2012.12.026</pub-id></element-citation></ref><ref id="B62-sensors-19-00762"><label>62.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Liu</surname><given-names>K.</given-names></name><name><surname>Zhao</surname><given-names>D.</given-names></name><name><surname>Fang</surname><given-names>J.-y.</given-names></name><name><surname>Zhang</surname><given-names>X.</given-names></name><name><surname>Zhang</surname><given-names>Q.-y.</given-names></name><name><surname>Li</surname><given-names>X.-k.</given-names></name></person-group><article-title>Estimation of heavy-metal contamination in soil using remote sensing spectroscopy and a statistical approach</article-title><source>J. Indian Soc. Remote Sens.</source><year>2016</year><volume>45</volume><fpage>805</fpage><lpage>813</lpage><pub-id pub-id-type="doi">10.1007/s12524-016-0648-4</pub-id></element-citation></ref><ref id="B63-sensors-19-00762"><label>63.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dong</surname><given-names>J.</given-names></name><name><surname>Dai</surname><given-names>W.</given-names></name><name><surname>Xu</surname><given-names>J.</given-names></name><name><surname>Li</surname><given-names>S.</given-names></name></person-group><article-title>Spectral estimation model construction of heavy metals in mining reclamation areas</article-title><source>Int. J. Environ. Res. Publ. Health</source><year>2016</year><volume>13</volume><elocation-id>640</elocation-id><pub-id pub-id-type="doi">10.3390/ijerph13070640</pub-id><pub-id pub-id-type="pmid">27367708</pub-id></element-citation></ref><ref id="B64-sensors-19-00762"><label>64.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Siebielec</surname><given-names>G.</given-names></name><name><surname>McCarty</surname><given-names>G.W.</given-names></name><name><surname>Stuczynski</surname><given-names>T.I.</given-names></name><name><surname>Reeves</surname><given-names>J.B.</given-names></name></person-group><article-title>Near- and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content</article-title><source>J. Environ. Qual.</source><year>2004</year><volume>33</volume><fpage>2056</fpage><pub-id pub-id-type="pmid">15537928</pub-id></element-citation></ref><ref id="B65-sensors-19-00762"><label>65.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mohamed</surname><given-names>E.S.</given-names></name><name><surname>Saleh</surname><given-names>A.M.</given-names></name><name><surname>Belal</surname><given-names>A.B.</given-names></name><name><surname>Gad</surname><given-names>A.A.</given-names></name></person-group><article-title>Application of near-infrared reflectance for quantitative assessment of soil properties</article-title><source>Egypt. J. Remote Sens. Space. Sci.</source><year>2018</year><volume>21</volume><fpage>1</fpage><lpage>14</lpage><pub-id pub-id-type="doi">10.1016/j.ejrs.2017.02.001</pub-id></element-citation></ref><ref id="B66-sensors-19-00762"><label>66.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Leone</surname><given-names>A.P.</given-names></name><name><surname>Sommer</surname><given-names>S.</given-names></name></person-group><article-title>Multivariate analysis of laboratory spectra for the assessment of soil development and soil degradation in the southern Apennines (Italy)</article-title><source>Remote Sens. Environ.</source><year>2000</year><volume>72</volume><fpage>346</fpage><lpage>359</lpage><pub-id pub-id-type="doi">10.1016/S0034-4257(99)00110-8</pub-id></element-citation></ref><ref id="B67-sensors-19-00762"><label>67.</label><element-citation publication-type="confproc"><person-group person-group-type="author"><name><surname>Bachari</surname><given-names>N.E.I.</given-names></name><name><surname>Khodja</surname><given-names>S.</given-names></name><name><surname>Belbachir</surname><given-names>A.</given-names></name></person-group><article-title>Multispectral analysis of satellite images</article-title><source>Proceedings of the XXth International Society for Photogrammetry and Remote Sensing (ISPRS) Congress: Geo-Imagery Bridging Continents</source><conf-loc>Istanbul, Turkey</conf-loc><conf-date>12–23 July 2004</conf-date><fpage>1071</fpage><lpage>1073</lpage></element-citation></ref></ref-list></back><floats-group><fig id="sensors-19-00762-f001" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Geographical position of the study area and locations of the 85 sampling points.</p></caption><graphic xlink:href="sensors-19-00762-g001"></graphic></fig><fig id="sensors-19-00762-f002" orientation="portrait" position="float"><label>Figure 2</label><caption><p>Illustration of the ASD high-intensity contact probe according to ASD Inc [<xref rid="B54-sensors-19-00762" ref-type="bibr">54</xref>]. X and Y are the height and width, respectively, of the Field of View (FoV).</p></caption><graphic xlink:href="sensors-19-00762-g002"></graphic></fig><fig id="sensors-19-00762-f003" orientation="portrait" position="float"><label>Figure 3</label><caption><p>Flowchart showing the methodology steps implemented in this study.</p></caption><graphic xlink:href="sensors-19-00762-g003"></graphic></fig><fig id="sensors-19-00762-f004" orientation="portrait" position="float"><label>Figure 4</label><caption><p>Mean spectra for soil samples characterised by low (sample 57) and high (sample 73) concentrations of heavy metals in the study site.</p></caption><graphic xlink:href="sensors-19-00762-g004"></graphic></fig><fig id="sensors-19-00762-f005" orientation="portrait" position="float"><label>Figure 5</label><caption><p>Mean (n = 85) variation in concentrations of the four heavy metals found in the study site.</p></caption><graphic xlink:href="sensors-19-00762-g005"></graphic></fig><fig id="sensors-19-00762-f006" orientation="portrait" position="float"><label>Figure 6</label><caption><p>Field-based spectral library of heavy metal soil contamination (HMSC) at the Bow Street site. Spectral regions related to water vapor absorption (1350–1430, 1790–1950 and 2400–2500 nm) have been removed.</p></caption><graphic xlink:href="sensors-19-00762-g006"></graphic></fig><fig id="sensors-19-00762-f007" orientation="portrait" position="float"><label>Figure 7</label><caption><p>Lab-based spectral library of the heavy metal soil contamination (HMSC) at the Bow Street site.</p></caption><graphic xlink:href="sensors-19-00762-g007"></graphic></fig><fig id="sensors-19-00762-f008" orientation="portrait" position="float"><label>Figure 8</label><caption><p>Wavelength-intervals shaded grey depict statistically significant differences between the field-based spectra. The red-dashed line denotes the limit for statistical significance (95% confidence level).</p></caption><graphic xlink:href="sensors-19-00762-g008"></graphic></fig><fig id="sensors-19-00762-f009" orientation="portrait" position="float"><label>Figure 9</label><caption><p>Wavelength-intervals shaded grey depict statistically significant differences between the lab-based spectra. The red-dashed line denotes the limit for statistical significance (95% confidence level).</p></caption><graphic xlink:href="sensors-19-00762-g009"></graphic></fig><table-wrap id="sensors-19-00762-t001" orientation="portrait" position="float"><object-id pub-id-type="pii">sensors-19-00762-t001_Table 1</object-id><label>Table 1</label><caption><p>Descriptive statistics of heavy metal concentrations in the soil of the contaminated area in Bow Street, UK, based on 85 samples. Max, Min, Median, Mean and Stdev are maximum, minimum, median, mean and standard deviation, respectively. The minimum detection limits of the atomic absorption spectrometer were 0.8, 1.5, 1.5 and 15 mg kg<sup>−1</sup> for Cd, Cu, Zn and Pb, respectively.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">mg kg<sup>−1</sup></th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Pb</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Zn</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Cu</th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Cd</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">Max</td><td align="center" valign="middle" rowspan="1" colspan="1">4600</td><td align="center" valign="middle" rowspan="1" colspan="1">361</td><td align="center" valign="middle" rowspan="1" colspan="1">249</td><td align="center" valign="middle" rowspan="1" colspan="1">2</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Min</td><td align="center" valign="middle" rowspan="1" colspan="1">220</td><td align="center" valign="middle" rowspan="1" colspan="1">82</td><td align="center" valign="middle" rowspan="1" colspan="1">13</td><td align="center" valign="middle" rowspan="1" colspan="1">1</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Median</td><td align="center" valign="middle" rowspan="1" colspan="1">670</td><td align="center" valign="middle" rowspan="1" colspan="1">140</td><td align="center" valign="middle" rowspan="1" colspan="1">32</td><td align="center" valign="middle" rowspan="1" colspan="1">1</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Mean</td><td align="center" valign="middle" rowspan="1" colspan="1">1100</td><td align="center" valign="middle" rowspan="1" colspan="1">156</td><td align="center" valign="middle" rowspan="1" colspan="1">47</td><td align="center" valign="middle" rowspan="1" colspan="1">1</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Stdev</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1037.959</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">59.850</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">42.869</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.204</td></tr></tbody></table></table-wrap><table-wrap id="sensors-19-00762-t002" orientation="portrait" position="float"><object-id pub-id-type="pii">sensors-19-00762-t002_Table 2</object-id><label>Table 2</label><caption><p>Summary of the selected spectral bands and regression coefficients for the field-based spectral library using stepwise multiple linear regression. A dash denotes that the spectral band was not included in the model equation for the relevant heavy metal.</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">Spectral Bands</th><th colspan="4" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">Model Coefficients for the Studied Heavy Metals</th></tr><tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Pb</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Zn</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Cu</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Cd</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">354 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">−320.758</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">366 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−64.043</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">367 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1">−81.125</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">368 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">42.275</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">374 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">71.865</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">386 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−57.897</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">388 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">90.868</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">389 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">456.742</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">64.551</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">393 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">66.374</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">394 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−96.782</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">434 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−23.652</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">582 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">−94.144</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">586 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−6.142</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">1348 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">0.965</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">1719 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">92.316</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">1775 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">−82.081</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">1951 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">0.008</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1978 nm</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.007</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Constant</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.172</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.139</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.026</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.001</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">R<sup>2</sup></td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.671</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.697</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.561</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.123</td></tr></tbody></table></table-wrap><table-wrap id="sensors-19-00762-t003" orientation="portrait" position="float"><object-id pub-id-type="pii">sensors-19-00762-t003_Table 3</object-id><label>Table 3</label><caption><p>Summary of the selected spectral bands and regression coefficients for the lab-based spectral library using stepwise multiple linear regression. A dash denotes that the spectral band was not included in the model equation for the relevant heavy metal.</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">Spectral Bands</th><th colspan="4" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">Coefficients of the Four Heavy Metals</th></tr><tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Pb</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Zn</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Cu</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Cd</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">356 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">90.729</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">358 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−4.369</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">359 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">2.502</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">368 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">5.055</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">376 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">9.101</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">470 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−78.747</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">475 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">127.870</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">484 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−53.910</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">618 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">−25.105</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">651 nm</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">−0.628</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1465 nm</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.001</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Constant</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.057</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.048</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.016</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.002</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">R<sup>2</sup></td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.641</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.642</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.428</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.048</td></tr></tbody></table></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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