Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy

  • Golayeh Yousefi
  • Mehdi HomaeeEmail author
  • Ali Akbar Norouzi


This study was aimed (i) to examine using diffuse reflectance spectra within VNIR region to estimate soil heavy metals concentrations at large scale, (ii) to compare the influence of different pre-processing models on predictive model accuracy, and (iii) to explore the best predictive models. A number of 325 topsoil samples were collected and their spectral data, pH, clay content, organic matter, Ni, and Cu concentrations were determined. To improve spectral data, various pre-processing methods including Savitzky-Golay smoothing filter, Savitzky-Golay smoothing filter with first and second derivatives, and standard normal variant (SNV) were used. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) models were employed to build calibration models for estimating soil heavy metals concentration followed by evaluation of provided predictive models. Results indicated that Cu had stronger correlation coefficients with spectral bands compared to Ni. Cu and Ni demonstrated strongest correlations at wavelengths 1925 and 1393 nm, respectively. Based on RMSE, R2, and RPD statistics, the PLSR model with Savitzky-Golay filter pretreatment provided the most accurate predictions for both Cu and Ni (R2 = 0.905, RMSE = 0.00123, RPD = 2.80 for Ni; R2 = 0.825, RMSE = 0.00467, RPD = 2.04 for Cu) where such prediction was much better for Ni than for Cu. Reasonable results with lower accuracy and stability were obtained for PCR (R2 = 0.742, RMSE = 0.00181, RPD = 1.91 for Ni; R2 = 0.731, RMSE = 0.00578, RPD = 1.65 for Cu) and SVMR (R2 = 0.643, RMSE = 0.00091, RPD = 3.80 for Ni; R2 = 0.505, RMSE = 0.00296, RPD = 3.22 for Cu). We concluded that reflectance spectroscopy technique could be applied as a reliable tool for detection and prediction of soil heavy metals.


Heavy metals Reflectance spectroscopy PLSR PCR SVMR 



This research was granted by Tarbiat Modares University, Grant Number IG-39713.


  1. Al Maliki, A., Owens, G., & Bruce, D. (2012). Capabilities of remote sensing hyperspectral images for the detection of lead contamination: a review. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 25 august–01 September, Australia, Melbourne. Volume I-7. doi: Scholar
  2. Asadi Kapourchal, S., Asadi Kapourchal, S., Pazira, E., & Homaee, M. (2009). Assessing radish potential for phytoremediation of lead- polluted soils resulting from air pollution. Plant Soil and Environment., 55(5), 202–206.CrossRefGoogle Scholar
  3. Atafar, Z., Mesdaghinia, A. R., Nouri, J., Homaee, M., Yunesian, M., Ahmadimoghadam, M., & Mahvi, A. H. (2010). Effect of fertilizer application on soil heavy metal concentration. Environmental Monitoring and Assessment., 160, 83–89.CrossRefGoogle Scholar
  4. Babaeian, E., Homaee, M., & Rahnemaei, R. (2016). Chelate-enhanced phytoextraction and phytostabilization of lead contaminated soils by carrot, Daucus Carrota. Archives of Agronomy and Soil Science, 62(3), 339–358.CrossRefGoogle Scholar
  5. Bauycos, G. J. (1962). Hydrometer method improved for making particle size of soils. Agronomy Journal, 56, 464–465. Scholar
  6. Ben-Dor, E., Chabrillat, S., Demattê, J. A. M., Taylor, G. R., Hill, J., Whiting, M. L., & Sommer, S. (2009). Using imaging spectroscopy to study soil properties. Remote Sensing of Environment, 113, S38–S55.CrossRefGoogle Scholar
  7. Brown, D. J., Shepherd, K. D., Walsh, M. G., Mays, M. D., & Reinsch, T. G. (2006). Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, 132, 273–290.CrossRefGoogle Scholar
  8. Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy-principal components regressionanalysis of soil properties. Soil Science Society of American Journal, 65, 480–490.CrossRefGoogle Scholar
  9. Davari, M., Homaee, M., & Rahnemaei, R. (2015a). An analytical deterministic model for simultaneous phytoremediation of Ni and Cd from contaminated soils. Environmental Science and Pollution Research, 22, 4609–4620.CrossRefGoogle Scholar
  10. Davari, M., Rahnemaei, R., & Homaee, M. (2015b). Competitive adsorption-desorption reactions of two hazardous heavy metals in contaminated soils. Environmental Science and Pollution Research, 22, 13024–13032.CrossRefGoogle Scholar
  11. Demmatê, J. A. M. (2002). Characterization and discrimination of soil by their reflected electromagnetic energy. Brazilian Journal of Agricultural Research, 37, 1445–1458.Google Scholar
  12. Gholizadeh, A., Borůvka, L., Vašát, R., Saberioon, M. M., Klement, A., Kratina, J., Tejnecký, V., & Drábek, O. (2015). Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: a case study. PLoS One, 10(2), 117–457. Scholar
  13. Eskandari, M., Homaee, M., & Falamaki, A. (2016). Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility. Environmental Science and Pollution Research., 23, 12423–12434.CrossRefGoogle Scholar
  14. Eskandari, M., Homaee, M., Mahmodi, S., Pazira, E., & van Genuchten, M. T. (2015). Optimizing landfill site selection by using land classification maps. Environmental Science and Pollution Research., 22, 7757–7765.CrossRefGoogle Scholar
  15. Eskandari, M., Homaee, M., & Mahmodi, S. (2012). An integrated multi criteria approach for landfill siting in a conflicting environmental, economical and socio-cultural area. Waste Management, 32(8), 1528–1538.CrossRefGoogle Scholar
  16. Farrokhian Firouzi, A., Homaee, M., Klumpp, E., Kasteel, R., & Tappe, W. (2015). Bacteria transport and retention in intact calcareous soil columns under saturated flow conditions. Journal of Hydrology and Hydromechanics, 62(2), 102–109.CrossRefGoogle Scholar
  17. Gras, J. P., Barthès, B. G., Mahaut, B., & Trupin, S. (2014). Best practices for obtaining and processing field visibleand near infrared (VNIR) spectra of topsoil. Geoderma, 215, 126–134.CrossRefGoogle Scholar
  18. Guerrero, C., ViscarraRossel, R. A., & Mouazen, A. M. (2010). Diffuse reflectance spectroscopy in soil science and land resource assessment. Geoderma, 158(1–2), 1–2. Scholar
  19. Hansen, E., Lassen, C., Stuer-Lauridsen, F., & Kjølholt, F. (2002). Heavy Metals in Waste. European Commission DG ENV.E3, Project ENV.E.3,ETU/200/0058, Final Report.Google Scholar
  20. Homaee, M., & Schmidhalter, U. (2008). Water integration by plants root under non-uniform soil salinity. Irrigation Science, 27, 83–95.CrossRefGoogle Scholar
  21. Jafarnejadi, A. R., Sayyad, G., Homaee, M., & Davamei, A. H. (2013). Spatial variability of soil total and DTPA-extractable cadmium caused by long-term application of phosphate fertilizers, crop rotation and soil characteristics. Environmental Monitoring and Assessment., 185, 4087–4096.CrossRefGoogle Scholar
  22. Jafarnejadi, A. R., Homaee, M., & Sayyad, G. (2011). Large scale spatial variability of accumulated cadmium in the wheat farm grains. Soil and Sediment Contamination Journal, 20(1), 93–99.Google Scholar
  23. Ji, J. F., Balsam, W., Chen, J., & Liu, L. W. (2002). Rapid and quantitative measurement of hematite and goethite in the Chinese loess-paleosol sequence by diffuse reflectance spectroscopy. Clays and Clay Minerals, 50(2), 208–216.CrossRefGoogle Scholar
  24. Kabata-Pendias, A., & Mukherjee, A. B. (2007). Trace elements from soil to human (p. 561). Berlin Heidelberg New York (NY): Springer. Trace elements from soil to human.CrossRefGoogle Scholar
  25. Kemper, T., & Sommer, S. (2002). Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environmental Science and Technology, 36(12), 2742–2747.CrossRefGoogle Scholar
  26. Khodaverdiloo, H., & Homaee, M. (2008). Modeling cadmium and lead phytoextraction from contaminated soils. Polish Journal of Soil Science, XLI(2), 149–162.Google Scholar
  27. Kooistra, L., Wehren, R., Buydens, L. M. C., Leuven, R. S. E. W., & Nienhuis, P. H. (2001a). Possibilities of soil spectroscopy for the classification of contaminated areas in river floodplains. International Journal of Applied Earth Observation and Geoinformation, 3(4), 337–344.CrossRefGoogle Scholar
  28. Kooistra, L., Wehrens, R., Leuven, R. S. E. W., & Buydens, L. M. C. (2001b). Possibilities of visible-near-infrared spectroscopy for the assessment of soil contamination in river flood plains. Analytica Chimica Acta, 446(1–2), 97–105.CrossRefGoogle Scholar
  29. Kooistra, L., Wanders, J., Eperma, G. F., Leuven, R. S. E. W., Wehrens, R., & Buydens, L. M. C. (2003). The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Analytica Chimica Acta, 484(2), 189–200.CrossRefGoogle Scholar
  30. Liu, Y. L., Chen, H., Wu, G. F., & Xu, X. G. (2011). Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—a case study along Le’an river, China. Geo-Spatial Information Science, 14(1), 10–16.CrossRefGoogle Scholar
  31. Malley, D. F., & Williams, P. C. (1997). Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environmental Science and Technology, 31(12), 3461–3467.CrossRefGoogle Scholar
  32. Martens, H., & Martens, M. (2000). Modified jack-knife estimation of parameter uncertainty in bilinear modeling by partial least squares regression (PLSR). Food Quality and Preference, 11(1–2), 5–16.CrossRefGoogle Scholar
  33. Mohammadi, S., Homaee, M., & Sadeghi, S. H. (2018). Runoff and sediment behavior from soil plots contaminated with kerosene and gasoil. Soil & Tillage Resarch, 182, 1–9.CrossRefGoogle Scholar
  34. Noshadi, E., & Homaee, M. (2018). Herbicides degradation kinetics in soil under different herbigation systems at field scale. Soil & Tillage Resarch, 184, 37–44.CrossRefGoogle Scholar
  35. Nouri, M., Homaee, M., & Bybordi, M. (2014). Quantitative assessment of LNAPLs retention in soil porous media. Soil and Sediment Contamination., 23, 801–819.CrossRefGoogle Scholar
  36. Pandit, M., Filippelli, M., & Li, L. (2010). Estimation of heavy metal contamination in soil using reflectance spectroscopy and partial least-squares regression. International Journal of Remote Sensing, 31(15), 4111–4123.CrossRefGoogle Scholar
  37. Plaza, A., Martínez, P., Pérez, R., & Plaza, J. (2002). Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remot Sensing, 40(9), 2025–2041. Scholar
  38. Rhoades, J. D. (1996). Methods of soil analysis: chemical methods: electrical conductivity and total dissolved solids (pp. 417–436). Wisconsin: ASA/SSSA Madison.Google Scholar
  39. Rossel, R. A. V., Cattle, S. R., Ortega, A., & Fouad, Y. (2009). In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy. Geoderma, 150(3–4), 253–266.CrossRefGoogle Scholar
  40. Rowell, D. L. (1994). Soil science: methods and applications (p. 345). Harlow: Longman Group.Google Scholar
  41. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639.CrossRefGoogle Scholar
  42. Shi, T., Chen, Y., Yaolin, L., & Wu, G. (2014). Visible and near-infrared reflectance spectroscopy—an alternative for monitoring soil contamination by heavy metals. Journal of Hazardous Material, 265, 166–176.CrossRefGoogle Scholar
  43. Sposito, G., Lund, L. J., & Chang, A. C. (1982). Trace metal chemistry in arid-zone field soils amended with sewage sludge: I. Fractionation of Ni, Cu, Zn, Cd and Pb in solid phases. Soil Science Society of America Journal, 46, 260–264.CrossRefGoogle Scholar
  44. Thomas, G. W. (1996). Methods of soil analysis: chemical methods: soil pH and soil acidity (pp 475–490). Wisconsin: ASA/SSSA Madison.Google Scholar
  45. Udelhoven, T., Emmerling, C., & Jarmer, T. (2003). Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: a feasibility study. Plant and Soil, 251(2), 319–329.CrossRefGoogle Scholar
  46. Viscarra Rossel, R. A. V. (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 90(1), 72–83.CrossRefGoogle Scholar
  47. Wang, J., Huang, C. P., Allen, H. E., Poesponegoro, I., Poesponegoro, H., & Takiyama, L. R. (1999). Effects of dissolved organic matter and pH on heavy metal uptake by sludge particulates exemplified by copper (II) and nickel (II): three-variable model. Water Environment Research, 71, 139–147.CrossRefGoogle Scholar
  48. Wang, J., Cui, L., Gao, W., Shi, T., Chen, Y., & Gao, Y. (2014). Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma, 216, 1–9.CrossRefGoogle Scholar
  49. Winkelmann, K. H. (2005). On the applicability of imaging spectrometry for the detection and investigation of contaminated sites with particular consideration given to the detection of fuel hydrocarbon contaminants in soil. Dissertation, Brandenburg university of technology.Google Scholar
  50. Wu, Y., Chen, J., Wu, X., Tian, Q., Ji, J., & Qin, Z. (2005). Possibilities of reflectance spectroscopy for assessment of contamination element in suburban soil. Applied Geochemistry, 20(6), 1051–1059.CrossRefGoogle Scholar
  51. Zhang, Z., Wen, J., & Zhao, D. (2010). Band selection method for retrieving soil lead content with hyperspectral remote sensing data. Proceeding (SPIE), earth resources and environmental remote sensing/GIS applications. Toulouse, France. p. 78311K-78311K-7.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Golayeh Yousefi
    • 1
  • Mehdi Homaee
    • 2
    Email author
  • Ali Akbar Norouzi
    • 3
  1. 1.College of AgricultureTarbiat Modares UniversityTehranIran
  2. 2.Department of Irrigation and DrainageTarbiat Modares UniversityTehranIran
  3. 3.Soil Conservation and Watershed Management Research Institute (SCWMRI)TehranIran

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