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

Yıl 2017, Cilt: 6 Sayı: 4, 365 - 373, 01.10.2017

Boško Miloš Aleksandra Bensa

https://doi.org/10.18393/ejss.319208

Cited By: 6

Öz

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, 2.42, 15.1 and 2.47 g C kg^-1
respectively, indicated successful 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. 

Anahtar Kelimeler

Chemometrics, PLSR, Spectral regions, wavelengths

Kaynakça

• 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.