Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • 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:https://doi.org/10.5194/isprsannals-I-7-55-2012.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Bauycos, G. J. (1962). Hydrometer method improved for making particle size of soils. Agronomy Journal, 56, 464–465. https://doi.org/10.2134/agronj1962.00021962005400050028x.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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 

  • 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. https://doi.org/10.1371/journal.pone.0117457.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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. https://doi.org/10.1016/j.geoderma.2010.05.008.

    Article  Google Scholar 

  • 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.

  • Homaee, M., & Schmidhalter, U. (2008). Water integration by plants root under non-uniform soil salinity. Irrigation Science, 27, 83–95.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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 

  • 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.

    Article  CAS  Google Scholar 

  • Kabata-Pendias, A., & Mukherjee, A. B. (2007). Trace elements from soil to human (p. 561). Berlin Heidelberg New York (NY): Springer. https://doi.org/10.5860/choice.45-2048 Trace elements from soil to human.

    Book  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Khodaverdiloo, H., & Homaee, M. (2008). Modeling cadmium and lead phytoextraction from contaminated soils. Polish Journal of Soil Science, XLI(2), 149–162.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Noshadi, E., & Homaee, M. (2018). Herbicides degradation kinetics in soil under different herbigation systems at field scale. Soil & Tillage Resarch, 184, 37–44.

    Article  Google Scholar 

  • Nouri, M., Homaee, M., & Bybordi, M. (2014). Quantitative assessment of LNAPLs retention in soil porous media. Soil and Sediment Contamination., 23, 801–819.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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. https://doi.org/10.1109/tgrs.2002.802494.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  CAS  Google Scholar 

  • Rowell, D. L. (1994). Soil science: methods and applications (p. 345). Harlow: Longman Group.

    Google Scholar 

  • Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Thomas, G. W. (1996). Methods of soil analysis: chemical methods: soil pH and soil acidity (pp 475–490). Wisconsin: ASA/SSSA Madison.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Viscarra Rossel, R. A. V. (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 90(1), 72–83.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. College of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran

    Golayeh Yousefi

  2. Department of Irrigation and Drainage, Tarbiat Modares University, Tehran, 14115-336, Iran

    Mehdi Homaee

  3. Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran

    Ali Akbar Norouzi

Authors
  1. Golayeh Yousefi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehdi Homaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Akbar Norouzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Homaee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousefi, G., Homaee, M. & Norouzi, A.A. Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy. Environ Monit Assess 190, 513 (2018). https://doi.org/10.1007/s10661-018-6898-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-018-6898-6

Keywords

Search

Navigation