Research Article
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Year 2023, , 244 - 256, 02.07.2023
https://doi.org/10.18393/ejss.1275149

Abstract

References

  • Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R.G., Wächter, D., Lee, J., Viscarra Rossel, R., Six, J., 2021. Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring. Soil 7(2): 525–546.
  • Bullock, P., Montanarella, L., 1987. Soil information : Uses and needs in Europe. European Soil Bureau Research Report No.9, pp. 397–417.
  • Burns, D.A., Ciurczak, E.W., 2007. Handbook of Near-Infrared Analysis. CRC Press, Boca Raton, 814p.
  • 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.
  • Deng, F., Minasny, B., Knadel, M., McBratney, A., Heckrath, G., Greve, M.H., 2013. Using Vis-NIR spectroscopy for monitoring temporal changes in soil organic carbon. Soil Science 178(8): 389–399.
  • 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.
  • Grunwald, S., Thompson, J.A., Boettinger, J.L., 2011. Digital soil mapping and modeling at continental scales: Finding solutions for global issues. Soil Science Society of America Journal 75(4): 1201–1213.
  • Guerrero, C., Wetterlind, J., Stenberg, B., Mouazen, A.M., Gabarrón-Galeote, M.A., Ruiz-Sinoga, J.D., Zornoza, R., Viscarra Rossel, R.A., 2016. Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy? Soil and Tillage Research 155: 501–509.
  • Janik, L.J., Merry, R.H., Forrester, S.T., Lanyon, D.M., Rawson, A., 2007. Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Science Society of America Journal 71(2): 507–514.
  • Janik, L.J., Merry, R.H., Skjemstad, J.O., 1998. Can mid infrared diffuse reflectance analysis replace soil extractions? Australian Journal of Experimental Agriculture 38(7): 681–696.
  • Janik, L.J., Skjemstand, J.O., Raven, M.D., 1995. Characterization and analysis of soils using mid-infrared partial least squares. I. correlations with xrf-determined major element composition. Australian Journal of Soil Research 33(4): 621–636.
  • Johnson, J.M., Vandamme, E., Senthilkumar, K., Sila, A., Shepherd, K.D., Saito, K., 2019. Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa. Geoderma 354: 113840.
  • Kaiser, M., Walter, K., Ellerbrock, R.H., Sommer, M., 2011. Effects of land use and mineral characteristics on the organic carbon content, and the amount and composition of Na-pyrophosphate-soluble organic matter, in subsurface soils. European Journal of Soil Science 62(2): 226–236.
  • 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.
  • Knox, N.M., Grunwald, S., McDowell, M.L., Bruland, G.L., Myers, D.B., Harris, W.G., 2015. Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239–240: 229–239.
  • Lorber, A., Wangen, L.E., Kowalski, B.R., 1987. A theoretical foundation for the PLS algorithm. Journal of Chemometrics 1(1): 19–31.
  • 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
  • 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., McBratney, A.B., 2008. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems 94(1): 72–79.
  • Næs, T., 1987. The design of calibration in near infra-red reflectance analysis by clustering. Journal of Chemometrics 1(2): 121–134.
  • 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.
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Development of Hungarian spectral library: Prediction of soil properties and applications

Year 2023, , 244 - 256, 02.07.2023
https://doi.org/10.18393/ejss.1275149

Abstract

Updating soil information systems (SIS) requires advanced technologies to support the time and cost-effective and environment-friendly soil data. The use of mid- infrared (MIR) Spectroscopy as alternative to wet chemistry has been tested. The MIR spectral library is a useful technique for predicting soil attributes with high accuracy, efficiency, and low cost. The Hungarian MIR spectral library contained data on 2200 soil samples from 10 counties representing the first Soil Information and Mentoring System (SIMS) survey. Archived soil samples were prepared and scanned based on Diffuse Reflectance Infrared spectroscopy (DRIFT) technique and spectra data were saved in the fourier transform infrared (FTIR) spectrometer OPUS software. Preprocessed filtering methods, outlier detection methods and calibration sample selection methods were applied for spectral library. MIR calibration models were built for soil attributes using Partial Least Square Regression (PLSR) method. Coefficient determination (R2), The Root Mean Squared Error (RMSE) and Ratio of Performance to Deviation (RPD) were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to significantly estimate soil properties such as SOC, CaCO3, sand, clay and silt through various scale models (national, county and soil type). The findings showed that our spectral library soil estimations are precise enough to provide information on national, county and soil type levels enabling a wide range of soil applications that demand huge amounts of data such as soil survey, precision agriculture and digital soil mapping.

References

  • Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R.G., Wächter, D., Lee, J., Viscarra Rossel, R., Six, J., 2021. Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring. Soil 7(2): 525–546.
  • Bullock, P., Montanarella, L., 1987. Soil information : Uses and needs in Europe. European Soil Bureau Research Report No.9, pp. 397–417.
  • Burns, D.A., Ciurczak, E.W., 2007. Handbook of Near-Infrared Analysis. CRC Press, Boca Raton, 814p.
  • 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.
  • Deng, F., Minasny, B., Knadel, M., McBratney, A., Heckrath, G., Greve, M.H., 2013. Using Vis-NIR spectroscopy for monitoring temporal changes in soil organic carbon. Soil Science 178(8): 389–399.
  • 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.
  • Grunwald, S., Thompson, J.A., Boettinger, J.L., 2011. Digital soil mapping and modeling at continental scales: Finding solutions for global issues. Soil Science Society of America Journal 75(4): 1201–1213.
  • Guerrero, C., Wetterlind, J., Stenberg, B., Mouazen, A.M., Gabarrón-Galeote, M.A., Ruiz-Sinoga, J.D., Zornoza, R., Viscarra Rossel, R.A., 2016. Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy? Soil and Tillage Research 155: 501–509.
  • Janik, L.J., Merry, R.H., Forrester, S.T., Lanyon, D.M., Rawson, A., 2007. Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Science Society of America Journal 71(2): 507–514.
  • Janik, L.J., Merry, R.H., Skjemstad, J.O., 1998. Can mid infrared diffuse reflectance analysis replace soil extractions? Australian Journal of Experimental Agriculture 38(7): 681–696.
  • Janik, L.J., Skjemstand, J.O., Raven, M.D., 1995. Characterization and analysis of soils using mid-infrared partial least squares. I. correlations with xrf-determined major element composition. Australian Journal of Soil Research 33(4): 621–636.
  • Johnson, J.M., Vandamme, E., Senthilkumar, K., Sila, A., Shepherd, K.D., Saito, K., 2019. Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa. Geoderma 354: 113840.
  • Kaiser, M., Walter, K., Ellerbrock, R.H., Sommer, M., 2011. Effects of land use and mineral characteristics on the organic carbon content, and the amount and composition of Na-pyrophosphate-soluble organic matter, in subsurface soils. European Journal of Soil Science 62(2): 226–236.
  • 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.
  • Knox, N.M., Grunwald, S., McDowell, M.L., Bruland, G.L., Myers, D.B., Harris, W.G., 2015. Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239–240: 229–239.
  • Lorber, A., Wangen, L.E., Kowalski, B.R., 1987. A theoretical foundation for the PLS algorithm. Journal of Chemometrics 1(1): 19–31.
  • 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
  • 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., McBratney, A.B., 2008. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems 94(1): 72–79.
  • Næs, T., 1987. The design of calibration in near infra-red reflectance analysis by clustering. Journal of Chemometrics 1(2): 121–134.
  • 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.
  • Pásztor, L., Laborczi, A., Takács, K., Szatmári, G., Dobos, E., Illés, G., Bakacsi, Z., Szabó, J., 2015. Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools. Hungarian Geographical Bulletin 64(1): 49–64.
  • 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/
  • Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Demattê, J.A.M., Scholten, T., 2013. The spectrum-based learner: A new local approach for modeling soil vis–NIR spectra of complex datasets. Geoderma 195–196: 268–279.
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There are 62 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Mohammed Ahmed Mohammedzein This is me 0009-0005-0043-7768

Adam Csorba This is me

Brian Rotich This is me

Phenson Nsima Justin This is me

Caleb Melenya This is me

Yuri Andrei This is me

Erika Micheli This is me

Publication Date July 2, 2023
Published in Issue Year 2023

Cite

APA Mohammedzein, M. A., Csorba, A., Rotich, B., Justin, P. N., et al. (2023). Development of Hungarian spectral library: Prediction of soil properties and applications. Eurasian Journal of Soil Science, 12(3), 244-256. https://doi.org/10.18393/ejss.1275149