Eurasian Journal of Soil Science

Volume 6, Issue 4, Sep 2017, Pages 365-373
DOI: 10.18393/ejss.319208
Stable URL: http://ejss.fess.org/10.18393/ejss.319208
Copyright © 2017 The authors and Federation of Eurasian Soil Science Societies



Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia

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Miloš,B., Bensa,A., 2017. Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian J Soil Sci 6(4):365-373. DOI : 10.18393/ejss.319208
Miloš,B.,& Bensa,A. (2017). Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia Eurasian Journal of Soil Science, 6(4):365-373. DOI : 10.18393/ejss.319208
Miloš,B., and ,Bensa,A. "Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia" Eurasian Journal of Soil Science, 6.4 (2017):365-373. DOI : 10.18393/ejss.319208
Miloš,B., and ,Bensa,A. "Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia" Eurasian Journal of Soil Science,6(Sep 2017):365-373 DOI : 10.18393/ejss.319208
B,Miloš.A,Bensa "Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia" Eurasian J. Soil Sci, vol.6, no.4, pp.365-373 (Sep 2017), DOI : 10.18393/ejss.319208
Miloš,Boško ;Bensa,Aleksandra Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian Journal of Soil Science, (2017),6.4:365-373. DOI : 10.18393/ejss.319208

How to cite

Miloš, B., Bensa, A., 2017. Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian J. Soil Sci. 6(4): 365-373. DOI : 10.18393/ejss.319208

Author information

Boško Miloš , Institute for Adriatic Crops and Karst Reclamation, Put Duilova 11, 21 000 Split, Croatia
Aleksandra Bensa , University of Zagreb, Faculty of Agriculture, Soil Science Department, Svetošimunska 25, 10 000 Zagreb, Croatia Zagreb, Crotia

Publication information

Article first published online : 07 Jun 2017
Manuscript Accepted : 24 May 2017
Manuscript Received: 09 Mar 2017
DOI: 10.18393/ejss.319208
Stable URL: http://ejss.fesss.org/10.18393/ejss.319208

Abstract

The objectives of this research were: (i) to assess the accuracy of diffuse reflectance spectroscopy (DRS) in predicting the soil organic carbon (SOC) content, and (ii) determine the importance of wavelength ranges and specific wavelengths in the SOC prediction model. The reflectance spectra of a total of 424 topsoils (0-25 cm) samples were measured in a laboratory using a portable Terra Spec 4 Hi-Res Mineral Spectrometer with a wavelength range 350-2500 nm. Partial least squares regression (PLSR) with leave-one-out cross validation was used to develop calibration models for SOC prediction. The accuracy of the estimate determined by the coefficient of determination (R2), the concordance correlation coefficient (ρc), the ratio of performance to deviation (RPD), the range error ratio (RER) and the root mean square error (RMSE) values of 0.83, 0.90, 2.22, 14.2 and 2.47 g C kg-1 respectively, indicated good model for SOC prediction. The near infrared (NIR) and the short-wave infrared (SWIR) spectrums were more accurate than those in the visible (VIS) and short-wave near-infrared (SWNIR) spectral regions. The wavelengths contributing most to the prediction of SOC were at: 1925, 1915, 2170, 2315, 1875, 2260, 1910, 2380, 435, 1960, 2200, 1050, 1420, 1425 and 500 nm. This study has shown that VIS-NIR reflectance spectroscopy can be used as a rapid method for determining organic carbon content in the Red Mediterranean soils that can be sufficient for a rough screening.

Keywords

Chemometrics, PLSR, Spectral regions, wavelengths

Corresponding author

References

Ben-Dor, E., Banin, A., 1995. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal 59(2): 364–372.

Ben-Dor, E., Irons, J., Epema, G., 1999. Soil reflectance. In: Remote sensing for the earth sciences: Manual of remote sensing, 3rd Edition Vol. 3.,  Rencz, A.N. (Ed.)., John Wilen Sons. Inc. New York, USA. pp. 111–188.

Brown, D.J., Shepherd, K.D., Walsh, M.G., May, M.D., Reinsch, T.G., 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132(3-4): 273–290.

 Chang, C.W., Laird, D.A., 2002. Near infrared reflectance spectroscopy analysis of soil C and N. Soil Science 167(2): 110-116.

Clark, R.N., 1999.  Spectroscopy of rocks and minerals, and principles of spectroscopy. In: Remote Sensing for the Earth Sciences: Manual of Remote Sensing, 3rd Edition Vol. 3., Rencz, A.N. (Ed.)., John Wilen Sons. Inc. New York, USA. pp. 3–58.

Clark, R.N., King, T.V.V., Klejwa, M., Swayze, G., Vergo, N., 1990. High spectral resolution reflectance spectroscopy of minerals.  Journal of Geophysical Research 95(B8): 12653 – 12680.

Dalal, R.C., Henry, R.J., 1986. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Science Society of America Journal 50(1): 120–123.

Demattê, J.A.M., Campos, R.C., Alvesb, M.C., Fiorioa, P.R., Nanni, M.R., 2004. Visible–NIR reflectance: a new approach on soil evaluation. Geoderma 121(1-2): 95 – 112.

Efron, B., Tibshirani, R.J., 1994.  An introduction to the bootstrap. Monographs on Statistics and Applied Probability 57. CRC. Press, Boca Raton, Florida, USA. 436p.

Fontán, J.M., López-Bellido, L., García-Olmo, J., López-Bellido, R.J., 2011. Soil carbon etermination in Mediterranean vertisol by visible and near infrared reflectance spectroscopy. Journal of  Near Infrared Spectroscopy  19(4): 253–263.

Gao, Y., Cui, L., Lei, B., Zhai, Y., Shi, T., Wang , J., Chen, Y., He, H., Wu, G., 2014.  Estimating soil organic carbon content with visible–near infrared (Vis–NIR) spectroscopy. Applied spectroscopy 68 (7): 712- 722.

Gras, J.P., Barthès, B.G., Mahaut, B., Trupin, S., 2014. Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils. Geoderma 214–215: 126–134.

Islam, K., Singh, B., McBratney, A., 2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Australian Journal of Soil Research 41(6): 1101–1114.

FAO, 2014. World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106, Food and Agriculture Organization of The United Nations (FAO) Rome, Italy. 192p.

JDPZ, 1966. Chemical methods for soil analysis, Beograd [in Croatian].

Knadel, M., Deng, F., Thomsen, A., Greve, M.H., 2012, Development of a Danish national vis-NIR soil spectral library for soil organic carbon determination. Digital Soil Assessments and Beyond. In: Proceedings of the 5th Global Workshop on Digital Soil Mapping. Minasny, B., Malone, B.P., McBratney, A.B., (Eds.). 10-13 April 2012, Sydney, Australia. Pp. 403- 408.

Kuang, B.,  Mouazen, A.M., 2012. Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale. European Journal of Soil Science 63(3): 421-429.

Lee, K.S., Lee, D.H., Sudduth, K.A., Chung, S.O., Kitchen, N.R., Drummond, S.T., 2009. Wavelength identification and diffuse reflectance estimation for surface and profile soil properties.  American Society of Agricultural and Biological Engineers 52(3): 683-695.

Leone, A.P., Viscarra-Rossel, R.A., Pietro Amenta, P., Buondonno, A., 2012.  Prediction of soil properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean soils from Southern Italy. Current Analytical Chemistry 8(2): 283-299.

Lin, L.I.K., 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1): 255-268.

Malley, D.F., Martin, P.D., Ben-Dor, E., 2004. Application in analysis of soils. In: Near-infrared spectroscopy in agriculture.  Roberts, C.A., Workman J., Reeves, J.B., (Eds.).  Agronomy Vol 44. ASA-CSSA-SSSA, Madison, WI, USA.  pp. 729-784.

Martens, H., Naes, T., 1989. Multivariate Calibration. John Wiley & Sons Inc. New York, USA. 504 p.

Miloš B. 2013. Spectral library of soils from Dalmatia. Institute for Adriatic Crops and Karst Reclamation. Split, Croatia. 

Saeys, W., Mouazen, A.M., Ramon, H., 2005. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosystems Engineering 91(4): 393–402.

Sarkhot, D.V., Grunwald, S.,  Ge, Y., Morgan, C.L.S., 2011. Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164(1-2): 22-32.

Savitzky, A., Golay, M.J.E., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8): 1627–1638.

Sherman, D.M., Waite, T.D., 1985. Electronic spectra of Fe3+ oxides and oxyhydroxides in the near IR to UV. American Mineralogist 70: 1262–1269.

Shi, Z., Ji, W., Viscarra Rossel, R.A., Chen, S., Zhou, Y., 2015. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis – NIR spectral library.  European Journal of Soil Science 66(4): 679–687.

Starr, C., Morgan, A.G., Smith, D.B., 1981. An evaluation of near infra-red reflectance analysis in some plant breeding programmes. Journal of Agricultural Science 97(1): 107-118.

Stenberg, B., Rossel, R.A.V., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. Advances in Agronomy 107: 163-215.

Stevens, A., Nocita, M., Tóth, G., Montanarella, L., van Wesemael, B., 2013. Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy. PLoS ONE 8(6): e66409.

Sudduth, K.A.,, Hummel, J.W., 1991. Evaluation of reflectance methods for soil organic matter sensing. Transactions of the ASAE 34(4): 1900–1909.

Summers, D., Lewis, M., Ostendorf, B., Chittleborough, D., 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators 11(1): 123-131.

Vasques, G.M., Grunwald, S., Harris, W.G.,  2010. Spectroscopic models of soil organic carbon in Florida, USA. Journal Environmental Quality 39(3): 923-934.

Viscarra Rossel, R.A., McGlynn, R.N., McBratney, A.B., 2006a. Determining the composition of mineral-organic mixes using UV-VIS-NIR diffuse reflectance spectroscopy. Geoderma 137(1-2): 70–82.

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

Viscarra Rossel, R.A., Behrens, T., 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1-2): 46–54.

Viscarra Rossel, R.A., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J.A.M., Shepherd, K.D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B.G., Bartholomeus, H.M.,  2016. A global spectral library to characterize the world's soil. Earth-Science Reviews 155: 198-230.

Wetterlind, J., Stenberg, B., Söderström, M., 2010. Increased sample point density in farm soil mapping by local calibration of visible and near infrared prediction models. Geoderma 156(3-4): 152–160.

Wijevardane, N., Ge, Y., Wills, S., Loecke, T., 2016. Prediction of soil carbon in the conterminous united states: visible and near-infrared reflectance spectroscopy analysis of the rapid carbon assessment project. Soil Science Society America Journal  80(4): 973-982.

Williams, P.C., 1987. Variables affecting near-infrared reflectance spectroscopic analysis. In: Near-infrared technology in the agricultural and food industries. Williams, P., Norris, K., (Eds.). American Association of Cereal Chemists, Saint Paul, USA. pp. 143 –167.

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

Xu, S., Shi, X., Wang, M., Zhao, Y., 2016. Effects of subsetting by parent materials on prediction of soil organic matter content in a hilly area using Vis–NIR spectroscopy. PLoS ONE 11(3): e0151536.

Abstract

The objectives of this research were: (i) to assess the accuracy of diffuse reflectance spectroscopy (DRS) in predicting the soil organic carbon (SOC) content, and (ii) determine the importance of wavelength ranges and specific wavelengths in the SOC prediction model. The reflectance spectra of a total of 424 topsoils (0-25 cm) samples were measured in a laboratory using a portable Terra Spec 4 Hi-Res Mineral Spectrometer with a wavelength range 350-2500 nm. Partial least squares regression (PLSR) with leave-one-out cross validation was used to develop calibration models for SOC prediction. The accuracy of the estimate determined by the coefficient of determination (R2), the concordance correlation coefficient (ρc), the ratio of performance to deviation (RPD), the range error ratio (RER) and the root mean square error (RMSE) values of 0.83, 0.90, 2.22, 14.2 and 2.47 g C kg-1 respectively, indicated good model for SOC prediction. The near infrared (NIR) and the short-wave infrared (SWIR) spectrums were more accurate than those in the visible (VIS) and short-wave near-infrared (SWNIR) spectral regions. The wavelengths contributing most to the prediction of SOC were at: 1925, 1915, 2170, 2315, 1875, 2260, 1910, 2380, 435, 1960, 2200, 1050, 1420, 1425 and 500 nm. This study has shown that VIS-NIR reflectance spectroscopy can be used as a rapid method for determining organic carbon content in the Red Mediterranean soils that can be sufficient for a rough screening.

Keywords: Chemometrics, PLSR, Spectral regions, wavelengths.

References

Ben-Dor, E., Banin, A., 1995. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal 59(2): 364–372.

Ben-Dor, E., Irons, J., Epema, G., 1999. Soil reflectance. In: Remote sensing for the earth sciences: Manual of remote sensing, 3rd Edition Vol. 3.,  Rencz, A.N. (Ed.)., John Wilen Sons. Inc. New York, USA. pp. 111–188.

Brown, D.J., Shepherd, K.D., Walsh, M.G., May, M.D., Reinsch, T.G., 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132(3-4): 273–290.

 Chang, C.W., Laird, D.A., 2002. Near infrared reflectance spectroscopy analysis of soil C and N. Soil Science 167(2): 110-116.

Clark, R.N., 1999.  Spectroscopy of rocks and minerals, and principles of spectroscopy. In: Remote Sensing for the Earth Sciences: Manual of Remote Sensing, 3rd Edition Vol. 3., Rencz, A.N. (Ed.)., John Wilen Sons. Inc. New York, USA. pp. 3–58.

Clark, R.N., King, T.V.V., Klejwa, M., Swayze, G., Vergo, N., 1990. High spectral resolution reflectance spectroscopy of minerals.  Journal of Geophysical Research 95(B8): 12653 – 12680.

Dalal, R.C., Henry, R.J., 1986. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Science Society of America Journal 50(1): 120–123.

Demattê, J.A.M., Campos, R.C., Alvesb, M.C., Fiorioa, P.R., Nanni, M.R., 2004. Visible–NIR reflectance: a new approach on soil evaluation. Geoderma 121(1-2): 95 – 112.

Efron, B., Tibshirani, R.J., 1994.  An introduction to the bootstrap. Monographs on Statistics and Applied Probability 57. CRC. Press, Boca Raton, Florida, USA. 436p.

Fontán, J.M., López-Bellido, L., García-Olmo, J., López-Bellido, R.J., 2011. Soil carbon etermination in Mediterranean vertisol by visible and near infrared reflectance spectroscopy. Journal of  Near Infrared Spectroscopy  19(4): 253–263.

Gao, Y., Cui, L., Lei, B., Zhai, Y., Shi, T., Wang , J., Chen, Y., He, H., Wu, G., 2014.  Estimating soil organic carbon content with visible–near infrared (Vis–NIR) spectroscopy. Applied spectroscopy 68 (7): 712- 722.

Gras, J.P., Barthès, B.G., Mahaut, B., Trupin, S., 2014. Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils. Geoderma 214–215: 126–134.

Islam, K., Singh, B., McBratney, A., 2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Australian Journal of Soil Research 41(6): 1101–1114.

FAO, 2014. World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106, Food and Agriculture Organization of The United Nations (FAO) Rome, Italy. 192p.

JDPZ, 1966. Chemical methods for soil analysis, Beograd [in Croatian].

Knadel, M., Deng, F., Thomsen, A., Greve, M.H., 2012, Development of a Danish national vis-NIR soil spectral library for soil organic carbon determination. Digital Soil Assessments and Beyond. In: Proceedings of the 5th Global Workshop on Digital Soil Mapping. Minasny, B., Malone, B.P., McBratney, A.B., (Eds.). 10-13 April 2012, Sydney, Australia. Pp. 403- 408.

Kuang, B.,  Mouazen, A.M., 2012. Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale. European Journal of Soil Science 63(3): 421-429.

Lee, K.S., Lee, D.H., Sudduth, K.A., Chung, S.O., Kitchen, N.R., Drummond, S.T., 2009. Wavelength identification and diffuse reflectance estimation for surface and profile soil properties.  American Society of Agricultural and Biological Engineers 52(3): 683-695.

Leone, A.P., Viscarra-Rossel, R.A., Pietro Amenta, P., Buondonno, A., 2012.  Prediction of soil properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean soils from Southern Italy. Current Analytical Chemistry 8(2): 283-299.

Lin, L.I.K., 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1): 255-268.

Malley, D.F., Martin, P.D., Ben-Dor, E., 2004. Application in analysis of soils. In: Near-infrared spectroscopy in agriculture.  Roberts, C.A., Workman J., Reeves, J.B., (Eds.).  Agronomy Vol 44. ASA-CSSA-SSSA, Madison, WI, USA.  pp. 729-784.

Martens, H., Naes, T., 1989. Multivariate Calibration. John Wiley & Sons Inc. New York, USA. 504 p.

Miloš B. 2013. Spectral library of soils from Dalmatia. Institute for Adriatic Crops and Karst Reclamation. Split, Croatia. 

Saeys, W., Mouazen, A.M., Ramon, H., 2005. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosystems Engineering 91(4): 393–402.

Sarkhot, D.V., Grunwald, S.,  Ge, Y., Morgan, C.L.S., 2011. Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164(1-2): 22-32.

Savitzky, A., Golay, M.J.E., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8): 1627–1638.

Sherman, D.M., Waite, T.D., 1985. Electronic spectra of Fe3+ oxides and oxyhydroxides in the near IR to UV. American Mineralogist 70: 1262–1269.

Shi, Z., Ji, W., Viscarra Rossel, R.A., Chen, S., Zhou, Y., 2015. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis – NIR spectral library.  European Journal of Soil Science 66(4): 679–687.

Starr, C., Morgan, A.G., Smith, D.B., 1981. An evaluation of near infra-red reflectance analysis in some plant breeding programmes. Journal of Agricultural Science 97(1): 107-118.

Stenberg, B., Rossel, R.A.V., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. Advances in Agronomy 107: 163-215.

Stevens, A., Nocita, M., Tóth, G., Montanarella, L., van Wesemael, B., 2013. Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy. PLoS ONE 8(6): e66409.

Sudduth, K.A.,, Hummel, J.W., 1991. Evaluation of reflectance methods for soil organic matter sensing. Transactions of the ASAE 34(4): 1900–1909.

Summers, D., Lewis, M., Ostendorf, B., Chittleborough, D., 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators 11(1): 123-131.

Vasques, G.M., Grunwald, S., Harris, W.G.,  2010. Spectroscopic models of soil organic carbon in Florida, USA. Journal Environmental Quality 39(3): 923-934.

Viscarra Rossel, R.A., McGlynn, R.N., McBratney, A.B., 2006a. Determining the composition of mineral-organic mixes using UV-VIS-NIR diffuse reflectance spectroscopy. Geoderma 137(1-2): 70–82.

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

Viscarra Rossel, R.A., Behrens, T., 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1-2): 46–54.

Viscarra Rossel, R.A., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J.A.M., Shepherd, K.D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B.G., Bartholomeus, H.M.,  2016. A global spectral library to characterize the world's soil. Earth-Science Reviews 155: 198-230.

Wetterlind, J., Stenberg, B., Söderström, M., 2010. Increased sample point density in farm soil mapping by local calibration of visible and near infrared prediction models. Geoderma 156(3-4): 152–160.

Wijevardane, N., Ge, Y., Wills, S., Loecke, T., 2016. Prediction of soil carbon in the conterminous united states: visible and near-infrared reflectance spectroscopy analysis of the rapid carbon assessment project. Soil Science Society America Journal  80(4): 973-982.

Williams, P.C., 1987. Variables affecting near-infrared reflectance spectroscopic analysis. In: Near-infrared technology in the agricultural and food industries. Williams, P., Norris, K., (Eds.). American Association of Cereal Chemists, Saint Paul, USA. pp. 143 –167.

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

Xu, S., Shi, X., Wang, M., Zhao, Y., 2016. Effects of subsetting by parent materials on prediction of soil organic matter content in a hilly area using Vis–NIR spectroscopy. PLoS ONE 11(3): e0151536.



Eurasian Journal of Soil Science