Eurasian Journal of Soil Science

Volume 12, Issue 4, Sep 2023, Pages 300 - 309
DOI: 10.18393/ejss.1309753
Stable URL: http://ejss.fess.org/10.18393/ejss.1309753
Copyright © 2023 The authors and Federation of Eurasian Soil Science Societies



Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library

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MohammedZein,M., Csorba,A., Rotich,B., Justin,P., Mohamed,H., Micheli,E., 2023. Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library. Eurasian J Soil Sci 12(4):300 - 309. DOI : 10.18393/ejss.1309753
MohammedZein,M.,Csorba,A.Rotich,B.Justin,P.Mohamed,H.,& Micheli,E. Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library Eurasian Journal of Soil Science, 12(4):300 - 309. DOI : 10.18393/ejss.1309753
MohammedZein,M.,Csorba,A.Rotich,B.Justin,P.Mohamed,H., and ,Micheli,E."Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library" Eurasian Journal of Soil Science, 12.4 (2023):300 - 309. DOI : 10.18393/ejss.1309753
MohammedZein,M.,Csorba,A.Rotich,B.Justin,P.Mohamed,H., and ,Micheli,E. "Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library" Eurasian Journal of Soil Science,12(Sep 2023):300 - 309 DOI : 10.18393/ejss.1309753
M,MohammedZein.A,Csorba.B,Rotich.P,Justin.H,Mohamed.E,Micheli "Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library" Eurasian J. Soil Sci, vol.12, no.4, pp.300 - 309 (Sep 2023), DOI : 10.18393/ejss.1309753
MohammedZein,Mohammed Ahmed ;Csorba,Adam ;Rotich,Brian ;Justin,Phenson Nsima ;Mohamed,Hanaa Tharwat ;Micheli,Erika Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library. Eurasian Journal of Soil Science, (2023),12.4:300 - 309. DOI : 10.18393/ejss.1309753

How to cite

MohammedZein, M., Csorba, A., Rotich, B., Justin, P., Mohamed, H., Micheli, E., 2023. Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library. Eurasian J. Soil Sci. 12(4): 300 - 309. DOI : 10.18393/ejss.1309753

Author information

Mohammed Ahmed MohammedZein , Institute of Environmental Sciences, Department of Soil Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary & Land and Water Research Center, Agricultural Research Corporation, Sudan
Adam Csorba , Institute of Environmental Sciences, Department of Soil Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
Brian Rotich , Institute of Environmental Sciences, Department of Soil Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
Phenson Nsima Justin , Institute of Environmental Sciences, Department of Soil Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
Hanaa Tharwat Mohamed , Institute of Environmental Sciences, Department of Soil Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
Erika Micheli , Institute of Environmental Sciences, Department of Soil Science, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary

Publication information

Article first published online : 05 Jun 2023
Manuscript Accepted : 20 May 2023
Manuscript Received: 01 Feb 2023
DOI: 10.18393/ejss.1309753
Stable URL: http://ejss.fesss.org/10.18393/ejss.1309753

Abstract

Routine soil chemical and physical laboratory analysis provides a better understanding of the soil by evaluating its quality and functions. Demands for the development of national Mid-infrared (MIR) spectral libraries for predicting soil attributes with high accuracy have risen substantially in the recent past. Such MIR spectral library is usually regarded as a fast, cheap and non-destructive technique for estimating soil properties compared to laboratory soil analysis. The main objective of this research was to assess the performance of the Hungarian MIR spectral library in estimating four soil properties namely: Cation Exchange Capacity (CEC), Exchangeable Mg and Ca and pH water at different scenarios. Archived soil samples were scanned and spectra data were saved in the Fourier transform infrared spectrometer OPUS software. Preprocessed filtering, outlier detection and calibration sample selection methods were applied to the spectral library. MIR calibration models were built for soil attributes using partial least square regression method and the models were validated with sample predictions. R2, RMSE and RPD were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to estimate soil properties such as CEC and exchangeable Ca and Mg through various scale models (national, county and soil type). The findings showed that the Hungarian MIR spectral library for estimation of soil properties has the ability to provide good information on national, county and soil type scales at different levels of accuracy.

Keywords

Mid-infrared spectroscopy, soil information monitoring system, partial least square regression, fourier transform infrared spectrometer, coefficient determination.

Corresponding author

References

D’Acqui, L.P., Pucci, A., Janik, L.J., 2010. Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid-infrared diffuse reflectance spectroscopy. European Journal of Soil Science 61(6): 865–876.

Demattê, J.A.M., Dotto, A.C., Bedin, L.G., Sayão, V.M., Souza, A.B., 2019. Soil analytical quality control by traditional and spectroscopy techniques: Constructing the future of a hybrid laboratory for low environmental impact. Geoderma 337: 111–121.

Demattê, J.A.M., Dotto, A.C., Paiva, A.F.S., Sato, M.V., Dalmolin, R.S.D., de Araújo, M.S.B., da Silva, E.B., Nanni, M.R., ten Caten, A., Noronha, N.C., Lacerda, M.P.C., de Araújo Filho, J.C., Rizzo, R., Bellinaso, H., Francelino, M.R., Schaefer, C.E.G.R., Vicente, L.E., dos Santos, U.J., de Sá Barretto Sampaio, E.V., Menezes, R.S.C., de Souza, J.J.L.L., Abrahão, W.A.P., Coelho, P.M., Grego, C.R., Lani, J.L., Fernandes, A.R., Gonçalves, D.A.M., Silva, S.H.G., de Menezes, M.D., Curi, N.C., Couto, E.G., dos Anjos, L.H.C., Ceddia, M.B., Pinheiro, E.F.M., Grunwald, S.G., Vasques, G.M., Júnior, J.M., da Silva, A.J., de Vasconcelos Barreto, M.J., Nóbrega, G.N., da Silva, M.Z., de Souza, S.F., Valladares, G.S., Viana, J.H.M., da Silva Terra, F., Horák-Terra, I., Fiorio, P.R., da Silva, R.C., Júnior, E.F.F., Lima, R.H.C., Alba, J.M.F., de Souza Junior, V.S., Brefin, M.L.M.S., Ruivo, M.L.P., Ferreira, T.O., Brait, M.A., Caetano, N.R., Bringhenti, I., Mendes, W.S., Safanelli, J.L., Guimarães, C.C.B., Poppiel, R.R., Souza, A.B., Quesada, C.A., do Couto, H.T.Z., 2019. The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges. Geoderma 354: 113793.

Dickens, A.A.. 2014. Standard operating procedures. Method for analysing samples for spectral characteristics in mid IR range using alpha. Code: METH07V02. World Agroforestry Centre, Nairobi, Kenya.

Kasprzhitskii, A., Lazorenko, G., Khater, A., Yavna, V., 2018. Mid-infrared spectroscopic assessment of plasticity characteristics of clay soils. Minerals 8(5): 184.

Kennard, R.W., Stone, L.A., 1969. Computer Aided Design of Experiments. Technometrics 11(1): 137-148.

Lorber, A., Wangen, L.E., Kowalski, B.R., 1987. A theoretical foundation for the PLS algorithm. Journal of Chemometrics 1(1): 19–31.

Madejová, J., 2003. FTIR techniques in clay mineral studies. Vibrational Spectroscopy 31(1): 1–10.

Max, K., Weston, S., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Team, R. C., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., 2016. Caret: classification and regression training. R package version 6.0-71. Available at [Access date : 26.01.2023]: https://CRAN.R-project.org/package=caret

McBratney, A., Field, D.J., Koch, A., 2014. The dimensions of soil security. Geoderma 213: 203–213.

Liland, K.H., Mevik, B.H., Wehrens, R., Hiemstra, P., 2016. Partial least squares and principal component regression. CRAN, 66p. Available at [Access date : 26.01.2023]: https://cran.r-project.org/web/packages/pls/pls.pdf

Minasny, B., McBratney, A.B., 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences 32(9): 1378–1388.

Minasny, B., Tranter, G., McBratney, A.B., Brough, D.M., Murphy, B.W., 2009. Regional transferability of mid-infrared diffuse reflectance spectroscopic prediction for soil chemical properties. Geoderma 153(1–2): 155–162.

Nash, D.B., 1986. Mid-infrared reflectance spectra (23–22 μm) of sulfur, gold, KBr, MgO, and halon. Applied Optics 25(14): 2427-2433.

Ng, W., Minasny, B., Jeon, S.H., McBratney, A., 2022. Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions. Soil Security 6: 100043.

Nguyen, T.T., Janik, L.J., Raupach, M., 1991. Diffuse reflectance infrared fourier transform (Drift) spectroscopy in soil studies. Australian Journal of Soil Research 29(1): 49–67.

Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., Dor, E. Ben, Brown, D. J., Clairotte, M., Csorba, A., Dardenne, P., Demattê, J. A. M., Genot, V., Guerrero, C., Knadel, M., Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., Sakai, H., Soriano-Disla, J.M., D. Shepherd, K.D., Stenberg, B., Towett, E.K., Vargas, R., Wetterlind, J., 2015. Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring. In: Advances in Agronomy. Sparks, D.L. (Ed.). Vol. 132, pp. 139–159.

Pirie, A., Singh, B., Islam, K., 2005. Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties. Australian Journal of Soil Research 43(6): 713–721.

R Core Team, 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at [Access date : 26.01.2023]: https://www.R-project.org/

Raphael, L., 2011. Application of FTIR spectroscopy to agricultural soils analysis. In: Fourier Transforms - New Analytical Approaches and FTIR Strategies. Nikolic, G. (Ed.). InTech. pp. 385-404.

Reeves, J.B., 2010. Near- versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: Where are we and what needs to be done? Geoderma 158(1–2): 3–14.

Reeves, J.B., Smith, D.B., 2009. The potential of mid- and near-infrared diffuse reflectance spectroscopy for determining major- and trace-element concentrations in soils from a geochemical survey of North America. Applied Geochemistry 24(8): 1472–1481.

Rossel, R.A.V., Jeon, Y.S., Odeh, I.O.A., McBratney, A.B., 2008. Using a legacy soil sample to develop a mid-IR spectral library. Australian Journal of Soil Research 46(1): 1–16.

Rossel, R.A.V., Webster, R., 2012. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. European Journal of Soil Science 63(6): 848–860.

Sarathjith, M.C., Das, B.S., Wani, S.P., Sahrawat, K.L., 2014. Dependency measures for assessing the covariation of spectrally active and inactive soil properties in diffuse reflectance spectroscopy. Soil Science Society of America Journal 78(5): 1522–1530.

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

Seybold, C.A., Ferguson, R., Wysocki, D., Bailey, S., Anderson, J., Nester, B., Schoeneberger, P., Wills, S., Libohova, Z., Hoover, D., Thomas, P., 2019. Application of mid‐infrared spectroscopy in soil survey. Soil Science Society of America Journal 83(6): 1746–1759.

Shepherd, K.D., Walsh, M.G., 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66(3): 988–998.

Shepherd, K.D., Walsh, M.G., 2007. Infrared spectroscopy—enabling an evidence-based diagnostic surveillance approach to agricultural and environmental management in developing countries. Journal of Near Infrared Spectroscopy 15(1): 1–19.

Siebielec, G., McCarty, G.W., Stuczynski, T.I., Reeves, J.B., 2004. Near- and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. Journal of Environmental Quality 33(6): 2056–2069.

Soriano-Disla, J.M., Janik, L.J., Viscarra Rossel, R.A., MacDonald, L.M., McLaughlin, M.J., 2014. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Applied Spectroscopy Reviews 49(2): 139–186.

Stenberg, B., Rossel, R.A.V., 2010. Diffuse reflectance spectroscopy for high-resolution soil sensing. In: Proximal Soil Sensing. Progress in Soil Science. Viscarra Rossel, R., McBratney, A., Minasny, B. (Eds.). Springer, Dordrecht. pp. 29–47.

Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. In: Advances in Agronomy. Sparks, D.L. (Ed.). Vol. 107, pp. 163–215.

Terhoeven-Urselmans, T., Vagen, T.G., Spaargaren, O., Shepherd, K.D., 2010. Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library. Soil Science Society of America Journal 74(5): 1792–1799.

TIM, 1995. Soil Conservation and Monitoring System. Ministry of Agriculture. Budapest, Hungary. Unpublished Report. [in Hungarian]

Tinti, A., Tugnoli, V., Bonora, S., Francioso, O., 2015. Recent applications of vibrational mid-Infrared (IR) spectroscopy for studying soil components: a review. Journal of Central European Agriculture 16(1): 1–22.

Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1–2): 59–75.

Wadoux, A., Malone, B., Minasny, B., Fajardo, M., Mcbratney, A.B., 2020. Soil spectral inference with R: Analysing digital soil spectra using the R programming environment. Springer Cham. 247p.

Waruru, B.K., Shepherd, K.D., Ndegwa, G.M., Sila, A., Kamoni, P.T., 2015. Application of mid-infrared spectroscopy for rapid characterization of key soil properties for engineering land use. Soils and Foundations 55(5): 1181–1195.

Waruru, B.K., Shepherd, K.D., Ndegwa, G.M., Kamoni, P.T., Sila, A.M., 2014. Rapid estimation of soil engineering properties using diffuse reflectance near infrared spectroscopy. Biosystems Engineering 121: 177–185.

Wijewardane, N.K., Ge, Y., Wills, S., Libohova, Z., 2018. Predicting physical and chemical properties of US soils with a mid-infrared reflectance spectral library. Soil Science Society of America Journal 82(3): 722–731.

Wold, S., Sjöström, M., Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58(2): 109–130.

Abstract

Routine soil chemical and physical laboratory analysis provides a better understanding of the soil by evaluating its quality and functions. Demands for the development of national Mid-infrared (MIR) spectral libraries for predicting soil attributes with high accuracy have risen substantially in the recent past. Such MIR spectral library is usually regarded as a fast, cheap and non-destructive technique for estimating soil properties compared to laboratory soil analysis. The main objective of this research was to assess the performance of the Hungarian MIR spectral library in estimating four soil properties namely: Cation Exchange Capacity (CEC), Exchangeable Mg and Ca and pH water at different scenarios. Archived soil samples were scanned and spectra data were saved in the Fourier transform infrared spectrometer OPUS software. Preprocessed filtering, outlier detection and calibration sample selection methods were applied to the spectral library. MIR calibration models were built for soil attributes using partial least square regression method and the models were validated with sample predictions. R2, RMSE and RPD were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to estimate soil properties such as CEC and exchangeable Ca and Mg through various scale models (national, county and soil type). The findings showed that the Hungarian MIR spectral library for estimation of soil properties has the ability to provide good information on national, county and soil type scales at different levels of accuracy.

Keywords: Mid-infrared spectroscopy, soil information monitoring system, partial least square regression, fourier transform infrared spectrometer, coefficient determination.

References

D’Acqui, L.P., Pucci, A., Janik, L.J., 2010. Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid-infrared diffuse reflectance spectroscopy. European Journal of Soil Science 61(6): 865–876.

Demattê, J.A.M., Dotto, A.C., Bedin, L.G., Sayão, V.M., Souza, A.B., 2019. Soil analytical quality control by traditional and spectroscopy techniques: Constructing the future of a hybrid laboratory for low environmental impact. Geoderma 337: 111–121.

Demattê, J.A.M., Dotto, A.C., Paiva, A.F.S., Sato, M.V., Dalmolin, R.S.D., de Araújo, M.S.B., da Silva, E.B., Nanni, M.R., ten Caten, A., Noronha, N.C., Lacerda, M.P.C., de Araújo Filho, J.C., Rizzo, R., Bellinaso, H., Francelino, M.R., Schaefer, C.E.G.R., Vicente, L.E., dos Santos, U.J., de Sá Barretto Sampaio, E.V., Menezes, R.S.C., de Souza, J.J.L.L., Abrahão, W.A.P., Coelho, P.M., Grego, C.R., Lani, J.L., Fernandes, A.R., Gonçalves, D.A.M., Silva, S.H.G., de Menezes, M.D., Curi, N.C., Couto, E.G., dos Anjos, L.H.C., Ceddia, M.B., Pinheiro, E.F.M., Grunwald, S.G., Vasques, G.M., Júnior, J.M., da Silva, A.J., de Vasconcelos Barreto, M.J., Nóbrega, G.N., da Silva, M.Z., de Souza, S.F., Valladares, G.S., Viana, J.H.M., da Silva Terra, F., Horák-Terra, I., Fiorio, P.R., da Silva, R.C., Júnior, E.F.F., Lima, R.H.C., Alba, J.M.F., de Souza Junior, V.S., Brefin, M.L.M.S., Ruivo, M.L.P., Ferreira, T.O., Brait, M.A., Caetano, N.R., Bringhenti, I., Mendes, W.S., Safanelli, J.L., Guimarães, C.C.B., Poppiel, R.R., Souza, A.B., Quesada, C.A., do Couto, H.T.Z., 2019. The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges. Geoderma 354: 113793.

Dickens, A.A.. 2014. Standard operating procedures. Method for analysing samples for spectral characteristics in mid IR range using alpha. Code: METH07V02. World Agroforestry Centre, Nairobi, Kenya.

Kasprzhitskii, A., Lazorenko, G., Khater, A., Yavna, V., 2018. Mid-infrared spectroscopic assessment of plasticity characteristics of clay soils. Minerals 8(5): 184.

Kennard, R.W., Stone, L.A., 1969. Computer Aided Design of Experiments. Technometrics 11(1): 137-148.

Lorber, A., Wangen, L.E., Kowalski, B.R., 1987. A theoretical foundation for the PLS algorithm. Journal of Chemometrics 1(1): 19–31.

Madejová, J., 2003. FTIR techniques in clay mineral studies. Vibrational Spectroscopy 31(1): 1–10.

Max, K., Weston, S., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Team, R. C., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., 2016. Caret: classification and regression training. R package version 6.0-71. Available at [Access date : 26.01.2023]: https://CRAN.R-project.org/package=caret

McBratney, A., Field, D.J., Koch, A., 2014. The dimensions of soil security. Geoderma 213: 203–213.

Liland, K.H., Mevik, B.H., Wehrens, R., Hiemstra, P., 2016. Partial least squares and principal component regression. CRAN, 66p. Available at [Access date : 26.01.2023]: https://cran.r-project.org/web/packages/pls/pls.pdf

Minasny, B., McBratney, A.B., 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences 32(9): 1378–1388.

Minasny, B., Tranter, G., McBratney, A.B., Brough, D.M., Murphy, B.W., 2009. Regional transferability of mid-infrared diffuse reflectance spectroscopic prediction for soil chemical properties. Geoderma 153(1–2): 155–162.

Nash, D.B., 1986. Mid-infrared reflectance spectra (23–22 μm) of sulfur, gold, KBr, MgO, and halon. Applied Optics 25(14): 2427-2433.

Ng, W., Minasny, B., Jeon, S.H., McBratney, A., 2022. Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions. Soil Security 6: 100043.

Nguyen, T.T., Janik, L.J., Raupach, M., 1991. Diffuse reflectance infrared fourier transform (Drift) spectroscopy in soil studies. Australian Journal of Soil Research 29(1): 49–67.

Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., Dor, E. Ben, Brown, D. J., Clairotte, M., Csorba, A., Dardenne, P., Demattê, J. A. M., Genot, V., Guerrero, C., Knadel, M., Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., Sakai, H., Soriano-Disla, J.M., D. Shepherd, K.D., Stenberg, B., Towett, E.K., Vargas, R., Wetterlind, J., 2015. Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring. In: Advances in Agronomy. Sparks, D.L. (Ed.). Vol. 132, pp. 139–159.

Pirie, A., Singh, B., Islam, K., 2005. Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties. Australian Journal of Soil Research 43(6): 713–721.

R Core Team, 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at [Access date : 26.01.2023]: https://www.R-project.org/

Raphael, L., 2011. Application of FTIR spectroscopy to agricultural soils analysis. In: Fourier Transforms - New Analytical Approaches and FTIR Strategies. Nikolic, G. (Ed.). InTech. pp. 385-404.

Reeves, J.B., 2010. Near- versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: Where are we and what needs to be done? Geoderma 158(1–2): 3–14.

Reeves, J.B., Smith, D.B., 2009. The potential of mid- and near-infrared diffuse reflectance spectroscopy for determining major- and trace-element concentrations in soils from a geochemical survey of North America. Applied Geochemistry 24(8): 1472–1481.

Rossel, R.A.V., Jeon, Y.S., Odeh, I.O.A., McBratney, A.B., 2008. Using a legacy soil sample to develop a mid-IR spectral library. Australian Journal of Soil Research 46(1): 1–16.

Rossel, R.A.V., Webster, R., 2012. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. European Journal of Soil Science 63(6): 848–860.

Sarathjith, M.C., Das, B.S., Wani, S.P., Sahrawat, K.L., 2014. Dependency measures for assessing the covariation of spectrally active and inactive soil properties in diffuse reflectance spectroscopy. Soil Science Society of America Journal 78(5): 1522–1530.

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

Seybold, C.A., Ferguson, R., Wysocki, D., Bailey, S., Anderson, J., Nester, B., Schoeneberger, P., Wills, S., Libohova, Z., Hoover, D., Thomas, P., 2019. Application of mid‐infrared spectroscopy in soil survey. Soil Science Society of America Journal 83(6): 1746–1759.

Shepherd, K.D., Walsh, M.G., 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66(3): 988–998.

Shepherd, K.D., Walsh, M.G., 2007. Infrared spectroscopy—enabling an evidence-based diagnostic surveillance approach to agricultural and environmental management in developing countries. Journal of Near Infrared Spectroscopy 15(1): 1–19.

Siebielec, G., McCarty, G.W., Stuczynski, T.I., Reeves, J.B., 2004. Near- and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. Journal of Environmental Quality 33(6): 2056–2069.

Soriano-Disla, J.M., Janik, L.J., Viscarra Rossel, R.A., MacDonald, L.M., McLaughlin, M.J., 2014. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Applied Spectroscopy Reviews 49(2): 139–186.

Stenberg, B., Rossel, R.A.V., 2010. Diffuse reflectance spectroscopy for high-resolution soil sensing. In: Proximal Soil Sensing. Progress in Soil Science. Viscarra Rossel, R., McBratney, A., Minasny, B. (Eds.). Springer, Dordrecht. pp. 29–47.

Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. In: Advances in Agronomy. Sparks, D.L. (Ed.). Vol. 107, pp. 163–215.

Terhoeven-Urselmans, T., Vagen, T.G., Spaargaren, O., Shepherd, K.D., 2010. Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library. Soil Science Society of America Journal 74(5): 1792–1799.

TIM, 1995. Soil Conservation and Monitoring System. Ministry of Agriculture. Budapest, Hungary. Unpublished Report. [in Hungarian]

Tinti, A., Tugnoli, V., Bonora, S., Francioso, O., 2015. Recent applications of vibrational mid-Infrared (IR) spectroscopy for studying soil components: a review. Journal of Central European Agriculture 16(1): 1–22.

Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1–2): 59–75.

Wadoux, A., Malone, B., Minasny, B., Fajardo, M., Mcbratney, A.B., 2020. Soil spectral inference with R: Analysing digital soil spectra using the R programming environment. Springer Cham. 247p.

Waruru, B.K., Shepherd, K.D., Ndegwa, G.M., Sila, A., Kamoni, P.T., 2015. Application of mid-infrared spectroscopy for rapid characterization of key soil properties for engineering land use. Soils and Foundations 55(5): 1181–1195.

Waruru, B.K., Shepherd, K.D., Ndegwa, G.M., Kamoni, P.T., Sila, A.M., 2014. Rapid estimation of soil engineering properties using diffuse reflectance near infrared spectroscopy. Biosystems Engineering 121: 177–185.

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