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

Department of Soil Science and Soil Protection Faculty of Agrobiology Food and Natural Resources Czech University of Life Sciences Prague Kamycka 129 Suchdol 16500 Prague Czech Republic

Department of Soil Science Luiz de Queiroz College of Agriculture University of Sao Paulo Padua Dias Avenue 11 CP 9 Piracicaba 13418 900 Brazil

Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Telegrafenberg 14473 Potsdam Germany

Remote Sensing Laboratory Department of Geography and Human Environment Porter School of Environment and Earth Science Tel Aviv University Tel Aviv 69978 Israel

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Shi T., Chen Y., Liu Y., Wu G. Visible and near-infrared reflectance spectroscopy—An alternative for monitoring soil contamination by heavy metals. J. Hazard. Mater. 2014;265:166–176. doi: 10.1016/j.jhazmat.2013.11.059. PubMed DOI

Khosravi V., Ardejani F.D., Yousefi S., Aryafar A. Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods. Geoderma. 2018;318:29–41. doi: 10.1016/j.geoderma.2017.12.025. DOI

Bruemmer G.W., Gerth J., Herms U. Heavy metal species, mobility and availability in soils. Z. für Pflanzenernährung und Bodenkd. 1986;149:382–398. doi: 10.1002/jpln.19861490404. DOI

García-Sánchez F., Galvez-Sola L., Martínez-Nicolás J.J., Muelas-Domingo R., Nieves M. Developments in Near-Infrared Spectroscopy. IntechOpen; London, UK: 2017. pp. 97–127. DOI

Gholizadeh A., Saberioon M., Ben-Dor E., Borůvka L. Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives. Crit. Rev. Environ. Sci. Technol. 2018;48:1–36. doi: 10.1080/10643389.2018.1447717. DOI

Gholizadeh A., Borůvka L., Vašát R., Saberioon M., Klement A., Kratina J., Tejnecký V., Drábek O. Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: A case study. PLoS ONE. 2015;10:e0117457. doi: 10.1371/journal.pone.0117457. PubMed DOI PMC

Shi T., Guo L., Chen Y., Wang W., Shi Z., Li Q., Wu G. Proximal and remote sensing techniques for mapping of soil contamination with heavy metals. Appl. Spectrosc. Rev. 2018;53:1–23. doi: 10.1080/05704928.2018.1442346. DOI

Viscarra Rossel R., Walter C. Rapid, quantitative and spatial field measurements of soil pH using an ion sensitive field effect transistor. Geoderma. 2004;119:9–20. doi: 10.1016/S0016-7061(03)00219-2. DOI

Sacristán D., Rossel R.A.V., Recatalá L. Proximal sensing of Cu in soil and lettuce using portable X-ray fluorescence spectrometry. Geoderma. 2016;265:6–11. doi: 10.1016/j.geoderma.2015.11.008. DOI

Viscarra Rossel R., Adamchuk V., Sudduth K., McKenzie N., Lobsey C. Chapter five proximal soil sensing: An effective approach for soil measurements in space and time. Adv. Agron. 2011;113:243–291. doi: 10.1016/b978-0-12-386473-4.00005-1. DOI

Stenberg B., Rossel R.A.V., Mouazen A.M., Wetterlind J. Chapter five visible and near infrared spectroscopy in soil science. Adv. Agron. 2010;107:163–215. doi: 10.1016/s0065-2113(10)07005-7. DOI

Gholizadeh A., Saberioon M., Ben-Dor E., Rossel R.A.V., Boruvka L. Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms. Environ. Pollut. 2020;267:115574. doi: 10.1016/j.envpol.2020.115574. PubMed DOI

Pozza L.E., Bishop T.F.A., Stockmann U., Birch G.F. Integration of vis-NIR and pXRF spectroscopy for rapid measurement of soil lead concentrations. Soil Res. 2020;58:247. doi: 10.1071/SR19174. DOI

Wu Y., Chen J., Wu X., Tian Q., Ji J., Qin Z. Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Appl. Geochem. 2005;20:1051–1059. doi: 10.1016/j.apgeochem.2005.01.009. DOI

Liu M., Wang T., Skidmore A.K., Liu X. Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images. Sci. Total Environ. 2018;637:18–29. doi: 10.1016/j.scitotenv.2018.04.415. PubMed DOI

Adler K., Piikki K., Söderström M., Eriksson J., Alshihabi O. Predictions of Cu, Zn, and Cd concentrations in soil using portable X-ray fluorescence measurements. Sensors. 2020;20:474. doi: 10.3390/s20020474. PubMed DOI PMC

Wang S.Q., Li W.D., Li J., Liu X.S. Prediction of Soil Texture Using FT-NIR Spectroscopy and PXRF Spectrometry with Data Fusion. Soil Sci. 2013;178:626–638. doi: 10.1097/SS.0000000000000026. DOI

Molin J.A.P., Tavares T.R. Sensor system for mapping soil fertility attributes: Challenges, Advances, and perspectives in Brazilian tropical soils. Eng. AgrÃcola. 2019;39:126–147. doi: 10.1590/1809-4430-eng.agric.v39nep126-147/2019. DOI

Wan M., Qu M., Hu W., Li W., Zhang C., Cheng H., Huang B. Estimation of soil pH using PXRF spectrometry and Vis-NIR spectroscopy for rapid environmental risk assessment of soil heavy metals. Process. Saf. Environ. Prot. 2019;132:73–81. doi: 10.1016/j.psep.2019.09.025. DOI

Xu D., Zhao R., Li S., Chen S., Jiang Q., Zhou L., Shi Z. Multi-sensor fusion for the determination of several soil properties in the Yangtze River Delta, China. Eur. J. Soil Sci. 2019;70:162–173. doi: 10.1111/ejss.12729. DOI

Zhang Y., Hartemink A.E. Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties. Eur. J. Soil Sci. 2019;71:316–333. doi: 10.1111/ejss.12875. DOI

Aldabaa A.A.A., Weindorf D.C., Chakraborty S., Sharma A., Li B. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma. 2015;239:34–46. doi: 10.1016/j.geoderma.2014.09.011. DOI

Chakraborty S., Weindorf D.C., Li B., Aldabaa A.A.A., Ghosh R.K., Paul S., Ali M.N. Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils. Sci. Total Environ. 2015;514:399–408. doi: 10.1016/j.scitotenv.2015.01.087. PubMed DOI

Xu D., Chen S., Xu H., Wang N., Zhou Y., Shi Z. Data fusion for the measurement of potentially toxic elements in soil using portable spectrometers. Environ. Pollut. 2020;263:114649. doi: 10.1016/j.envpol.2020.114649. PubMed DOI

Ji W., Adamchuk V.I., Chen S., Su A.S.M., Ismail A., Gan Q., Shi Z., Biswas A. Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study. Geoderma. 2019;341:111–128. doi: 10.1016/j.geoderma.2019.01.006. DOI

Xu D., Chen S., Rossel R.V., Biswas A., Li S., Zhou Y., Shi Z. X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content. Geoderma. 2019;352:61–69. doi: 10.1016/j.geoderma.2019.05.036. DOI

O’Rourke S., Minasny B., Holden N., McBratney A. Synergistic use of Vis-NIR, MIR, and XRF spectroscopy for the determination of soil geochemistry. Soil Sci. Soc. Am. J. 2016;80:888–899. doi: 10.2136/sssaj2015.10.0361. DOI

Mouazen A., Kuang B., Baerdemaeker J.D., Ramon H. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma. 2010;158:23–31. doi: 10.1016/j.geoderma.2010.03.001. DOI

Nawar S., Buddenbaum H., Hill J., Kozak J., Mouazen A.M. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil Tillage Res. 2016;155:510–522. doi: 10.1016/j.still.2015.07.021. DOI

Borůvka L., Vácha R. Litavka river alluvium as a model area heavily polluted with potentially risk elements. In: Morel J.L., Echevarria G., Goncharova N., editors. Phytoremediation of Metal-Contaminated Soils. Springer; Dordrecht, The Netherlands: 2006. pp. 267–298.

Famera M., Kotkova K., Tumova S., Elznicova J., Matys Grygar T. Pollution distribution in floodplain structure visualised by electrical resistivity imaging in the floodplain of the Litavka River, the Czech Republic. Catena. 2018;165:157–172. doi: 10.1016/j.catena.2018.01.023. DOI

IUSS Working Group WRB . World Reference Base for Soil Resources 2014, Update 2015: International Soil Classification System for Naming Soils and Creating Legends for Soil Maps. FAO; Rome, Italy: 2015. Technical Report.

Sparks D. Methods of Soil Analysis. Part 3–Chemical Methods. Soil Science Society of America, American Society of Agronomy; Madison, WI, USA: 1996.

Ben-Dor E., Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci. Soc. Am. J. 1995;59:364–372. doi: 10.2136/sssaj1995.03615995005900020014x. DOI

Fujii K., Ikeda S., Akama A., Komatsu M., Takahashi M., Kaneko S. Vertical migration of radiocesium and clay mineral composition in five forest soils contaminated by the Fukushima nuclear accident. Soil Sci. Plant Nutr. 2014;60:751–764. doi: 10.1080/00380768.2014.926781. DOI

Hewavitharana A.K. Matrix matching in liquid chromatography-mass spectrometry with stable isotope labelled internal standards–is it necessary? J. Chromatogr. 2011;1218:359–361. doi: 10.1016/j.chroma.2010.11.047. PubMed DOI

Ben-Dor E., Ong C., Lau I.C. Reflectance measurements of soils in the laboratory: Standards and protocols. Geoderma. 2015;245:112–124. doi: 10.1016/j.geoderma.2015.01.002. DOI

Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964;36:1627–1639. doi: 10.1021/ac60214a047. DOI

Gholizadeh A., Žižala D., Saberioon M., Borůvka L. Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sens. Environ. 2018;218:89–103. doi: 10.1016/j.rse.2018.09.015. DOI

Gholizadeh A., Borůvka L., Saberioon M., Vašát R. Visible, near-Infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: State-of-the-art and key issues. Appl. Spectrosc. 2013;67:1349–1362. doi: 10.1366/13-07288. PubMed DOI

Aggarwal C.C. Outlier ensembles: Position paper. ACM SIGKDD Explor. Newsl. 2013;14:49–58. doi: 10.1145/2481244.2481252. DOI

Kebonye N.M., John K., Chakraborty S., Agyeman P.C., Ahado S.K., Eze P.N., Němeček K., Drábek O., Borůvka L. Comparison of multivariate methods for arsenic estimation and mapping in floodplain soil via portable X-ray fluorescence spectroscopy. Geoderma. 2021;384:114792. doi: 10.1016/j.geoderma.2020.114792. DOI

Xiaobo Z., Jiewen Z., Povey M.J., Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta. 2010;667:14–32. doi: 10.1016/j.aca.2010.03.048. PubMed DOI

Solorio-Fernández S., Carrasco-Ochoa J.A., Martínez-Trinidad J.F. A review of unsupervised feature selection methods. Artif. Intell. Rev. 2020;53:907–948. doi: 10.1007/s10462-019-09682-y. DOI

Reda R., Saffaj T., Ilham B., Saidi O., Issam K., Brahim L., Hadrami E.E. A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy. Chemom. Intell. Lab. Syst. 2019;195:103873. doi: 10.1016/j.chemolab.2019.103873. DOI

Padungweang P., Lursinsap C., Sunat K. Univariate filter technique for unsupervised feature selection using a new Laplacian score based local nearest neighbors; Proceedings of the 2009 IEEEAsia-Pacific Conference on Information Processing; Shenzhen, China. 18–19 July 2009; pp. 196–200. DOI

Vohland M., Besold J., Hill J., Fründ H.C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma. 2011;166:198–205. doi: 10.1016/j.geoderma.2011.08.001. DOI

Leardi R., González A.L. Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemom. Intell. Lab. Syst. 1998;41:195–207. doi: 10.1016/S0169-7439(98)00051-3. DOI

Yoshida H., Leardi R., Funatsu K., Varmuza K. Feature selection by genetic algorithms for mass spectral classifiers. Anal. Chim. Acta. 2001;446:483–492. doi: 10.1016/S0003-2670(01)00910-2. DOI

Murthy C.A., Chowdhury N. In search of optimal clusters using genetic algorithms. Pattern Recognit. Lett. 1996;17:825–832. doi: 10.1016/0167-8655(96)00043-8. DOI

Kuhn M. Building predictive models in R using the caret package. J. Stat. Softw. Artic. 2008;28:1–26. doi: 10.18637/jss.v028.i05. DOI

Viscarra Rossel R., Walvoort D., McBratney A., Janik L., Skjemstad J. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma. 2006;131:59–75. doi: 10.1016/j.geoderma.2005.03.007. DOI

Wan M., Hu W., Qu M., Li W., Zhang C., Kang J., Hong Y., Chen Y., Huang B. Rapid estimation of soil cation exchange capacity through sensor data fusion of portable XRF spectrometry and Vis-NIR spectroscopy. Geoderma. 2020;363:114163. doi: 10.1016/j.geoderma.2019.114163. DOI

Kooistra L., Wanders J., Epema G., Leuven R., Wehrens R., Buydens L. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal. Chim. Acta. 2003;484:189–200. doi: 10.1016/S0003-2670(03)00331-3. DOI

Gholizadeh A., Boruvka L., Saberioon M., Vasat R. A memory-based learning approach as compared to other data mining algorithms for the prediction of soil texture using diffuse reflectance Spectra. Remote Sens. 2016;8:341. doi: 10.3390/rs8040341. DOI

Gholizadeh A., Saberioon M., Carmon N., Boruvka L., Ben-Dor E. Examining the performance of PARACUDA-II data-mining engine versus selected techniques to model soil carbon from reflectance spectra. Remote Sens. 2018;10:1172. doi: 10.3390/rs10081172. DOI

Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media; Berlin/Heidelberg, Germany: 2009.

Bellon-Maurel V., Fernandez-Ahumada E., Palagos B., Roger J.M., McBratney A. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends Anal. Chem. 2010;29:1073–1081. doi: 10.1016/j.trac.2010.05.006. DOI

Gomez C., Gholizadeh A., Borůvka L., Lagacherie P. Using legacy data for correction of soil surface clay content predicted from VNIR/SWIR hyperspectral airborne images. Geoderma. 2016;276:84–92. doi: 10.1016/j.geoderma.2016.04.019. DOI

Suchara I., Sucharová J. Distribution of sulphur and heavy metals in forest floor humus of the Czech Republic. Water Air Soil Pollut. 2002;136:289–316. doi: 10.1023/A:1015235924991. DOI

Sucharová J., Suchara I., Hola M., Reimann C., Boyd R., Filzmoser P., Englmaier P. Linking chemical elements in forest floor humus (Oh-horizon) in the Czech Republic to contamination sources. Environ. Pollut. 2011;159:1205–1214. doi: 10.1016/j.envpol.2011.01.041. PubMed DOI

Borůvka L., Sramek V., Cupr P., Fadrhonsova V., Hofman J., Houska J., Sanka O., Slavikova A.A., Sindelarova L., Tejnecky V., et al. Srovnávací Hodnoty pro Hodnocení Kontaminace Lesních pud: Certifikovaná Metodika. Výzkumný ústav Lesního Hospodářství a Myslivosti; Strnady, Czech Republic: 2015.

Pavlů L., Drábek O., Borůvka L., Nikodem A., Němeček K. Degradation of forest soils in the vicinity of an industrial zone. Soil Water Res. 2016;10:65–73. doi: 10.17221/220/2014-SWR. DOI

Wilding L. Spatial variability: Its documentation, accommodation and implication to soil surveys; Proceedings of the Soil Spatial Variability; Las Vegas, NV, USA. 30 November–1 December 1984; pp. 166–194.

Xu S., Zhao Y., Wang M., Shi X. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma. 2018;310:29–43. doi: 10.1016/j.geoderma.2017.09.013. DOI

Hu B., Chen S., Hu J., Xia F., Xu J., Li Y., Shi Z. Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution. PLoS ONE. 2017;12:1–13. doi: 10.1371/journal.pone.0172438. PubMed DOI PMC

Chang C.W., Laird D.A., Mausbach M.J., Hurburgh C.R. Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 2001;65:480–490. doi: 10.2136/sssaj2001.652480x. DOI

Jia J., Song Y., Yuan X., Yang Z. Diffuse reflectance spectroscopy study of heavy metals in agricultural soils of the Changjiang river delta, China; Proceedings of the 19th World Congress of Soil Science; Brisbane, Australia. 1–6 August 2010.

Wu Y., Chen J., Ji J., Gong P., Liao Q., Tian Q., Ma H. A Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils. Soil Sci. Soc. Am. J. 2007;71:918–926. doi: 10.2136/sssaj2006.0285. DOI

Heller Pearlshtien D., Ben-Dor E. Effect of Organic Matter Content on the Spectral Signature of Iron Oxides across the VIS–NIR Spectral Region in Artificial Mixtures: An Example from a Red Soil from Israel. Remote Sens. 2020;12:1960. doi: 10.3390/rs12121960. DOI

Hong Y., Shen R., Cheng H., Chen Y., Zhang Y., Liu Y., Zhou M., Yu L., Liu Y., Liu Y. Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest. Sci. Total Environ. 2018;651:1969–1982. doi: 10.1016/j.scitotenv.2018.09.391. PubMed DOI

Richter N., Jarmer T., Chabrillat S., Oyonarte C., Hostert P., Kaufmann H. Free iron oxide determination in Mediterranean soils using diffuse reflectance spectroscopy. Soil Sci. Soc. Am. J. 2009;73:72–81. doi: 10.2136/sssaj2008.0025. DOI

Simon M., Martin F., Ortiz I., Garcia I., Fernandez J., Fernandez E., Dorronsoro C., Aguilar J. Soil pollution by oxidation of tailings from toxic spill of a pyrite mine. Sci. Total Environ. 2001;279:63–74. doi: 10.1016/S0048-9697(01)00726-4. PubMed DOI

Horta A., Malone B., Stockmann U., Minasny B., Bishop T., McBratney A., Pallasser R., Pozza L. Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma. 2015;241–242:180–209. doi: 10.1016/j.geoderma.2014.11.024. DOI

Estienne F., Massart D., Zanier-Szydlowski N., Marteau P. Multivariate calibration with Raman spectroscopic data: A case study. Anal. Chim. Acta. 2000;424:185–201. doi: 10.1016/S0003-2670(00)01107-7. DOI

Vohland M., Emmerling C. Determination of total soil organic C and hot water-extractable C from VIS-NIR soil reflectance with partial least squares regression and spectral feature selection techniques. Eur. J. Soil Sci. 2011;62:598–606. doi: 10.1111/j.1365-2389.2011.01369.x. DOI

Vohland M., Michel K., Ludwig B. Use of near-infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest-floor material in soils: Usefulness of a genetic algorithm. J. Plant Nutr. Soil Sci. 2011;174:695–701. doi: 10.1002/jpln.201000226. DOI

Laiho J., Peramaki P. Evaluation of portable X-ray fluorescence (PXRF) sample preparation methods. Spec. Pap.-Geol. Surv. Finl. 2005;38:73.