Precision Agriculture

, Volume 12, Issue 3, pp 395–420 | Cite as

Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially

  • A. Volkan Bilgili
  • Fevzi Akbas
  • Harold M. van Es


Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.


Soil variation Visible near infrared reflectance spectroscopy (VNIRRS) Partial least square regression (PLSR) Kriging Cokriging Regression kriging 


  1. 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, 364–372.CrossRefGoogle Scholar
  2. Bishop, T. F. A., & McBratney, A. B. (2001). A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma, 103, 149–160.CrossRefGoogle Scholar
  3. Bourennane, H., Dere, Ch., Lamy, I., Cornu, S., Baize, D., van Oort, F., et al. (2006). Enhancing spatial estimates of metal pollutants in raw wastewater irrigated fields using a topsoil organic carbon map predicted from aerial photography. Science of the Total Environment, 361, 229–248.PubMedCrossRefGoogle Scholar
  4. Bouyoucos, G. J. (1926). Estimation of the colloidal material in soils. Science, 64, 362.PubMedCrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. Burgess, T. M., Webster, R., & McBratney, A. B. (1981). Optimal interpolation and isarithmic mapping of soil properties. IV sampling strategy. Journal of Soil Science, 32, 643–659.CrossRefGoogle Scholar
  7. Chang, C. W., Laird, D. A., Mausbach, M. J., Maurice, J., & Hurburgh, J. R. (2001). Near-infrared reflectance spectroscopy—Principal components regression analyses of soil properties. Soil Science Society of America Journal, 65, 480–490.CrossRefGoogle Scholar
  8. Chodak, M., Ludwig, B., Khanna, P., & Beese, F. (2002). Use of near infrared spectroscopy to determine biological and chemical characteristics of organic layers under spruce and beech stands. Journal of Plant Nutrition and Soil Science, 165, 27–33.CrossRefGoogle Scholar
  9. Cozzolino, D., & Morón, A. (2003). The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics. Journal of Agricultural Science, 140, 65–71.CrossRefGoogle Scholar
  10. 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, 120–123.CrossRefGoogle Scholar
  11. Dunn, B. W., Beecher, H. G., Batten, G. D., & Ciavarella, S. (2002). The potential of near-infrared reflectance spectroscopy for soil analysis—A case study from the Riverine Plain of south-eastern Australia. Australian Journal of Experiment Agriculture, 42, 607–614.CrossRefGoogle Scholar
  12. Ersahin, S. (2003). Comparing ordinary kriging and cokriging to estimate infiltration rate. Soil Science Society of America Journal, 67, 1848–1855.CrossRefGoogle Scholar
  13. Ge, Y., Thomasson, J. A., Morgan, C. L., & Searcy, S. W. (2007). VNIR diffuse reflectance spectroscopy for agricultural soil property determination based on regression-kriging. Transactions of the ASABE, 50, 1081–1092.Google Scholar
  14. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.Google Scholar
  15. Hengl, T., Heuvelink, G. B. M., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers and Geosciences, 33, 1301–1315.CrossRefGoogle Scholar
  16. Hengl, T., Heuvelink, G. B. M., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120, 75–93.CrossRefGoogle Scholar
  17. Idowu, O. J., van Es, H. M., Abawi, G. S., Wolfe, D. W., Ball, J. I., Gugino, B. K., et al. (2008). Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods. Plant and Soil, 307, 243–253.CrossRefGoogle Scholar
  18. Islam, K., Singh, B., Schwenke, G., & McBratney, A. B. (2004). Evaluation of vertosol soil fertility using ultra-violet, visible and near-infrared reflectance spectroscopy. In B. Singh (Ed.), SuperSoil 2004: 3rd Australian New Zealand soil conference. Symposium 4: Emerging soil analytical techniques in the laboratory and the field. Sydney, Australia: University of Sydney.Google Scholar
  19. Janzen, H. H. (1993). Soluble salts. In M. R. Carter (Ed.), Soil sampling and methods of analysis (pp. 161–166). Boca Raton, FL: CRC Press Inc.Google Scholar
  20. Kacar, B. (1994). Soil and plant analysis III—Soil analysis. No. 3. Ankara, Turkey: Faculty of Agriculture, University of Ankara.Google Scholar
  21. Kerry, R., & Oliver, M. A. (2007). Determining the effect of asymmetric data on the variogram. I. Underlying asymmetry. Computers and Geosciences, 33, 1212–1232.CrossRefGoogle Scholar
  22. Kravchenko, A. N. (2003). Influence of spatial structure on accuracy of interpolation methods. Soil Science Society America Journal, 67, 1564–1571.CrossRefGoogle Scholar
  23. Kravchenko, A. N., & Robertsen, G. P. (2007). Can topographical and yield data substantially improve total soil carbon mapping by regression kriging ? Agronomy Journal, 99, 12–17.CrossRefGoogle Scholar
  24. Laslett, G. M. (1994). Kriging and splines: An emprical comparison of their predictive performances in some applications. Journal of American Statistical Association, 89, 391–400.CrossRefGoogle Scholar
  25. Ludwig, B., Khanna, P. K., Bauhus, P., & Hopmans, P. (2002). Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability. Forest Ecology and Management, 171, 121–132.CrossRefGoogle Scholar
  26. Martens, H., & Naes, T. (1989). Multivariate calibration. Chichester, UK: Wiley.Google Scholar
  27. Matheron, G. (1965). Les variables régionalisées et leur estimation: une application de la théorie de fonctions aléatoires aux sciences de la nature. Paris: Masson et Cie.Google Scholar
  28. McBratney, A. B., & Webster, R. (1983). Coregionalization and multiple sampling strategy. Journal of Soil Science, 34, 249–263.CrossRefGoogle Scholar
  29. McLean, E. O. (1982). Soil pH and lime requirement. In A. L. Page, R. H. Miller, & D. R. Keeney (Eds.), Methods of soil analysis (Part II) (pp. 199–223). Agronomy Monography No: 9. Madison, WI: ASA SSSA.Google Scholar
  30. Mueller, T. G., Pierce, F. J., Schabenberger, O., & Warncke, D. D. (2001). Map quality for site specific fertility management. Soil Science Society America Journal, 65, 1547–1558.CrossRefGoogle Scholar
  31. Mueller, T. G., Pusuluri, N. B., Mathias, K. K., Cornelius, P. L., Barnhisel, R. I., & Shearer, S. A. (2004). Map quality for ordinary kriging and inverse distance weighted interpolation. Soil Science Society America Journal, 68, 2042–2047.CrossRefGoogle Scholar
  32. Nelson, D. W., & Sommers, L. E. (1982). Total carbon, organic carbon and organic matter. In A. L. Page, R. H. Miller, & D. R. Keeney (Eds.), Methods of soil analysis (Part II) (pp. 570–571). Agronomy Monography No: 9. Madison, WI: ASA SSSA.Google Scholar
  33. Odeh, I. O. A., & McBratney, A. B. (1995). Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma, 67, 215–226.CrossRefGoogle Scholar
  34. Odeh, I. O. A., McBratney, A. B., & Chittleborough, D. J. (1994). Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma, 63, 197–214.CrossRefGoogle Scholar
  35. Odeh, I. O. A., McBratney, A. B., & Chittleborough, D. J. (2004). Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma, 63, 197–215.CrossRefGoogle Scholar
  36. R Development Core Team. (2006). R; A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.Google Scholar
  37. Reeves, J., McCarty, G., & Mimmo, T. (2002). The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution, 116, 277–284.CrossRefGoogle Scholar
  38. Reeves, J. B., & Van Kessel, J. S. (1999). Investigations into near-infrared analysis as an alternative to traditional procedures in manure N and C mineralization studies. Journal Near Infrared Spectroscopy, 7, 197–212.Google Scholar
  39. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least square procedure. Analytical Chemistry, 36, 1627–1639.CrossRefGoogle Scholar
  40. Shepherd, K. D., & Walsh, M. G. (2002). Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society America Journal, 66, 988–998.CrossRefGoogle Scholar
  41. Sullivan, D. G., Shaw, J. N., & Rickman, D. (2005). IKONOS imagery to estimate surface soil property in two Alabama physiographies. Soil Science America Journal, 69, 1789–1798.CrossRefGoogle Scholar
  42. Takata, Y., Funakawa, S., Akshalov, K., Ishida, N., & Kosaki, T. (2007). Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information. Soil Science and Plant Nutrition, 53, 289–299.CrossRefGoogle Scholar
  43. Tarr, A. B., Kenneth, J. M., & Dixon, P. M. (2005). Spectral reflectance as a covariate for estimating pasture productivity and composition. Crop Science Society of America, 45, 996–1003.Google Scholar
  44. Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperspectral data. Remote Sensing Environment, 66, 41–51.CrossRefGoogle Scholar
  45. 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, 319–329.CrossRefGoogle Scholar
  46. Viscarra Rossel, R. A., & McBratney, A. B. (1998). Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content. Geoderma, 85, 19–39.CrossRefGoogle Scholar
  47. 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, 59–75.CrossRefGoogle Scholar
  48. Voltz, M., & Webster, R. (1990). A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. Journal of Soil Science, 41, 473–490.CrossRefGoogle Scholar
  49. Warrick, A. W., Myers, D. E., & Nielsen, D. R. (1986). Geostatistical methods applied to soil science. In A. Klute (Ed.), Methods of soil analysis (Part I) (pp. 53–82). Agronomy Monography No: 9. Madison, WI: ASA SSSA.Google Scholar
  50. Webster, R., & Oliver, M. A. (1992). Sampling adequately to estimate variograms of soil properties. Journal of Soil Science, 43, 177–192.CrossRefGoogle Scholar
  51. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Chichester, England: Wiley.CrossRefGoogle Scholar
  52. Wu, Y. Z., Chen, J., Ji, J. F., Tian, Q. J., & Wu, X. M. (2005). Feasibility of reflectance spectroscopy for the assessment of soil mercury contamination. Environmental Science and Technology, 39, 873–878.PubMedCrossRefGoogle Scholar
  53. Wu, J., Norvell, W. A., & Welch, R. M. (2006). Kriging on highly skewed data for DTPA-extractable soil Zn with auxiliary information for pH and organic carbon. Geoderma, 134, 187–199.CrossRefGoogle Scholar
  54. Yıldız, H. (1997). Detailed soil survey and mapping of Tokat fruit production stations soils. (In Turkish, with English abstract.) MSc Thesis, Gaziosmanpasa University, Tokat, Turkey.Google Scholar
  55. Zhang, R., Warrick, A. W., & Myers, D. E. (1992). Improvement of the prediction of soil particle-size fractions using spectral properties. Geoderma, 52, 223–234.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • A. Volkan Bilgili
    • 1
  • Fevzi Akbas
    • 2
  • Harold M. van Es
    • 3
  1. 1.Department of Soil Science, Agriculture FacultyHarran UniversitySanliurfaTurkey
  2. 2.Department of Soil Science, Agriculture FacultyGaziosmanpasa UniversityTokatTurkey
  3. 3.Department of Crop and Soil ScienceCornell UniversityIthacaUSA

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