Comparing Different Statistical Models and Pre-processing Techniques for Estimation several chemical properties of the soil Using VNIR/SWIR Spectrum

Document Type : علمی - پژوهشی

Authors

1 College of Agriculture, Lorestan University, Lorestan, Iran and Faculty, Department of Agriculture, Payame Noor University.

2 College of Agriculture, Lorestan University, Lorestan, Iran

3 Agriculture Faculty, Shahid Bahonar University of Kerman, Kerman, , Iran

4 Faculty of Agriculture and Natural Resources, Ardakan University, Yazd, Iran.

5 Faculty of Agriculture, Isfahan university of Technology,Isfahan,Iran

Abstract

Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm), which are at least as costly and time-consuming, are widely used in the estimation of physical and chemical properties of the soil. The purpose of this study was to investigate the ability of this method to estimate the amount of organic matter, carbonates and gypsum content of soil surface. In the present study, 115 profiles were identified based on the Hypercube technique, and the horizons were sampled and the amount of organic matter, carbonates and gypsum content were measured by standard methods. Reflectance spectra of all samples were measured using an ASD field-portable spectrometer in the laboratory. Soil samples were divided into two random groups (80% and 20%) for calibration and validation of models. PLSR and PCR models and different pre-processing methods i.e. First (FD) and Second Derivatives (SD), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were applied and compared to estimate texture elements. The highest RPD of calibration and validation were obtained for PLSR with First derivative of reflectance+ Savitzky_Golay filter pre-processing technique which was classified as a good for the amount of organic matter and gypsum and was classified as a poorly for the amount of carbonates.

Keywords

References

  1. Aїchi, H.Y., Fouad, C., Walter, R.A., Viscarra Rossel, Z.L., Chabaane and Sanaa M., 2009, Regional predictions of soil organic carbon content from spectral reflectance measurements, Biosys. Eng. 104: 442-446.
  2. Araújo, S.R., Demattê ,J.A.M., Franceschini, M.H.D., Rizzo, R., Stenberg, B. & Wetterlind, J., 2013, Improving the predictive performance of a national vis-NIR spectroscopic library by comparing clustering data transformation, and data-mining calibration techniques, p. 2431-2440. Anais XVI SimposioBrasileirode Sensoriamento Remoto, 13 - 18 April. 2013. Foz do Iguaçu, PR, Brazil.
  3. Ben-Dor, E. & Banin, A., 1995, Near-infrared analysis (NIRA) as a method to simultaneously evaluate spectral featureless constituents in soils, Soil Science, 159:259–270.
  4. Barnes, R.J., Dhanoa, M.S. & Lister, S.J., 1989, Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra, Applied Spectroscopy, 43:772-777.
  5. Camo, AS Press ., 2006, The Unscrambler Tutorials. CAMO Software Research & Development Team, CAMO Software, NedreVollgate 8, N-0158, Oslo, Norway, retrieved on March 20/2012 from http://www.camo.com/
  6. Cambou, A., Cardinael, R., Kouakoua, E., Villeneuve, M., Durand, C. & Barthès, B.G., 2015, Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field, Geoderma 261, 151–159.
  7. Chang, C.W., Laird, D.A., Mausbach, M.J., and Hurburgh, C.R., 2001, Near-infrared reflectance spectroscopyprincipal com-ponents regression analyses of soil properties, Soil Science Society of America Journal, 65:480–490.
  8. Chen, L., Sheng-lu, Z., Shao-hua, W., Qing, Z. & Qi, D., 2014, Spectral Response of Different Eroded Soils in Subtropical China: A Case Study in Changting County, China, Journal of Materials Science, 11: 697-707.
  9. Curcio, D., Ciraolo, G., D’Asaro, F. &Minacapillia, M., 2013, Prediction of soil texture distributions using VNIRSWIR reflectance spectroscopy, Procedia Environmental Sciences, 19:494 – 503.
  10. Debaene, G., Niedźwiecki, J. & Pecio, A., 2010, Visible and near-infrared spectrophotometer for soil analysis: preliminary results, Pol. J. Agronom. 3: 3–9.
  11. Farmer, V.C. & Russell, J.D., 1964, The infrared spectra of layer silicates, Spectrochim. Acta. 20: 1149-1173.
  12. Gomez, C., Rossel ,V. & McBratney, B., 2008, Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study, Geoderma, 146:403–411.
  13. Gholizadeh, A., Boruvkai, L., Saberioon, M.M., Kozaki, J., Vasati, R. & Nemeki, K., 2015, Comparing Different Data Preprocessing, Soil & Water Res., 10:218–227.
  14. Khayamim, F., Khademi, H., Stenberg, B. & Wetterlind, J., 2015, Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province, JWSS - Isfahan University of Technology. 19 (72): 81-92.
  15. Klement, A., Jaksik, O., Kodesova, R., Drabek, O. & Boruvka, L., 2013, Application of VNIR diffuse reflectance spectroscopy to estimate soil organic carbon content, and content of different forms of iron and manganese, Geophysical Research Abstracts, 15:10846-1.
  16. Lagacherie, P., Baret, F., Feret, J.B., Netto ,J.M. & Robbez-Masson, J.M., 2008, Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements, Remote Sensing of Environment, 112:825–835.
  17. 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, 52:683-695.
  18. Liu, Y., Jiang, Q., Fei, T., Wang, J., Shi, T., Guo, K., Li, X. & Chen, Y., 2014, Transferability of a Visible and Near- Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes, Remote Sensing, 6:4305-4322.
  19. Martens, H. & Næs, T., 1989, Multivariate Calibration, John Wiley & Sons: Chichester, United Kingdom.
  20. McCoy, R.M., 2005, Field Methods in Remote Sensing, A Division of Guilford Publications, Inc. Spring, NewYork, U.S, pp. 67-87.
  21. McDowell, M.L., Bruland, G.L., Deenik, J.L., Grunwald, S. & Knox, N.M., 2012, Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy, Geoderma, 189:312-320.
  22. Mouazen, A.M., Kuang, B., DeBaerdemaeker, J. & Ramon, H., 2010, Comparison between principal components, partial least squares and artificial neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared 455 spectroscopy, Geoderma, 158:23-31.
  23. Rinnan, A., Berg, F.V.D. & Engelsen, S.B., 2009, Review of the most common pre-processing techniques for near-infrared spectra, Trends in Analytical Chemistry, 28: 1201-1222.
  24. Schwartz, G., Eshel, G. & Ben-Dor, E., 2011, Reflectance spectroscopy as a tool for monitoring contaminated soils, p. 67-90. In S. Pascucci (ed.) Soil Contamination, In Technology.
  25. Sharma, S., Goodarzi, M., Ramon, H. & Saeys, W., 2014, Performance evaluation of preprocessing techniques utilizing expert information in multivariate calibration, Talanta, 121:105-12.
  26. Shepherd, K.D. & Walsh, M.G., 2002, Development of reflectance spectral libraries for characterization of soil properties, Soil Sci. Soc. Am. J. 66, 988–998.
  27. Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M. & Wetterlind, J., 2010, Visible and near infrared spectroscopy in soil science, Adv. Agron. 107, 163–215.
  28. Shiferaw, A., and Hergarten, Ch., 2014, Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia, Journal of Ecology and the Natural Environment, 6:126-139.
  29. Summers, D., Lewis, M., Ostendorf, B. & Chittleborough, D., 2011, Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties, Ecological Indicators, 11:123-131.
  30. Stenberg, B., Jonsson, A. & Börjesson, T., 2002, Near infrared technology for soil analysis with implications for precision agriculture, PP. 279–284. In: A. Davies and R. Cho, (Eds.), Near Infrared Spectroscopy: Proceeding of 10th International Conference. NIR Publications, Chichester, UK.
  31. Viscarra Rossel, R.A. & Behrens, T., 2010, Using data mining to model and interpret soil diffuse reflectance spectra, Geoderma 158, 46–54.
  32. Viscarra Rossel, R.A., Taylor, H.J. & McBratney, A.B., 2007, Multivariate calibration of hyperspectral γ-ray energy spectra for proximal soil sensing, European Journal of Soil Science, 58:343-353.
  33. Wold, S., Martens, H. & Wold, H., 1983, The multivariate calibration problem in chemistry solved by the PLS method, Matrix Pencils.

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