News

a.z
Rasooli, N., Farpoor, M., Khayamim, F., Ranjbar, H. (2018). Prediction of Selected Soil Properties Using Visible and Near Infrared Spectroscopy in Bardsir Area, Kerman Province. , 32(2), 231-242. doi: 10.22092/ijsr.2018.117044
N. Rasooli; M. H. Farpoor; F. Khayamim; H. Ranjbar. "Prediction of Selected Soil Properties Using Visible and Near Infrared Spectroscopy in Bardsir Area, Kerman Province". , 32, 2, 2018, 231-242. doi: 10.22092/ijsr.2018.117044
Rasooli, N., Farpoor, M., Khayamim, F., Ranjbar, H. (2018). 'Prediction of Selected Soil Properties Using Visible and Near Infrared Spectroscopy in Bardsir Area, Kerman Province', , 32(2), pp. 231-242. doi: 10.22092/ijsr.2018.117044
Rasooli, N., Farpoor, M., Khayamim, F., Ranjbar, H. Prediction of Selected Soil Properties Using Visible and Near Infrared Spectroscopy in Bardsir Area, Kerman Province. , 2018; 32(2): 231-242. doi: 10.22092/ijsr.2018.117044

Prediction of Selected Soil Properties Using Visible and Near Infrared Spectroscopy in Bardsir Area, Kerman Province

پژوهش های خاک
Article 8, Volume 32, Issue 2, September 2018, Pages 231-242    PDF (1020.59 K)
Document Type: Research Paper
DOI: 10.22092/ijsr.2018.117044
Authors
N. Rasooli1; M. H. Farpoor* 2; F. Khayamim3; H. Ranjbar4
1PhD, Student, Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2Professor, Dept. of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman; Kerman, Iran
3PhD, Dept. of Soil Science, Faculty of Agriculture, Isfahan University of Technology, Isfahan, Iran
4Professor, Dept. of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
Soil spectroscopy in the visible and near infrared (Vis-NIR) range has widely been used as a rapid, cost-effective, and non-destructive technique to predict soil properties. Since little data is available about soil properties determined by using this technique, the present research was carried out to evaluate the efficiency of Vis-NIR spectroscopy to estimate several soil properties in Bardsir area, Kerman Province. About 150 complex surface soil samples were collected from four different land uses from depth of 0-20 cm. Soil organic carbon, equivalent calcium carbonate, pH, and the amount of silt, clay and sand particles were measured by routine laboratory methods. Reflectance spectra were obtained from air-dried samples under controlled laboratory conditions using an ASD FieldSpec Pro spectroradiometer in 350-2500 nm wavelength range. Partial least squares regression was used for calibration of spectral and laboratory data using cross validation. Coefficient of variation for organic carbon, equivalent calcium carbonate, sand, silt, clay, and pH values were 0.68, 0.62, 0.64, 0.66, 0.3, and 0.01, respectively. Based on RPD values (Ratio of Prediction to Deviation), the precision of the prediction model for sand and silt contents was quite suitable, and for organic carbon and equivalent calcium carbonate it was suitable. [H1] However, the predictions of the model for clay content and pH were poor.Furthermore, standard normal variate (SNV) was the best pre-processing method to predict organic carbon, whereas, first derivative with SG smoothing (FD-SG) showed better estimation for carbonate, sand, and silt. Consequently, Vis-NIR spectroscopy is capable of predicting several soil properties at the same time. As the model accuracy is acceptable, it has the potential to substitute conventional laboratory analyses of selected soil properties.



 [H1]این تغییرات با توجه به متن فارسی انجام شد.
Keywords
Validation; Spectral pre-processing; Partial least-squares regression; Reflective spectroscopy
References
  1. Babaeian, E., M. Homaee, H. Vereecken, C. Montzka, A. Norouzi, and M.van Genuchten. 2015. A Comparative Study of Multiple Approaches for Predicting the Soil-Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties. Soil Science Society of America Journal. 79:1043-1058.
  2. Ben-Dor, E., Y. Inbar, and Y. Chen. 1997. The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during acontrolled decomposition process. Remote Sens. Environ. 61(1):1-15.
  3. Bilgili, A., H. van Es, F. Akbas, and A. Durak. 2010. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. Journal of Arid Environments, 74(2): 229-238.
  4. Bouyoucos, G. 1962. Hydrometer method improved for making particle size analysis of soils. Agron. J. 54:464-465.
  5. Buddenbaum, H., and M. Steffens. 2012. The effects of spectral pretreatments on chemometric analyses of soil profiles using laboratory imaging spectroscopy. Applied and Environmental Soil Science. 1-12.
  6. Carnieletto Dotto, A., R. Dalmolin, A. Caten, and S. Grunwald . 2017a. A systematic study on the application of scatter-corrective and spectral derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma. 314:262-274.
  7. Carnieletto Dotto, A., R. Dalmolina, S. Grunwaldb, A. Catenc, and W. Filhod . 2017b. Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy. Soil & Tillage Research. 172:59-68.
  8. Chang, C.W., D.A. Laird, M.J. Mausbach, and C.R. Hurburgh. 2001. Near-Infrared Reflectance Spectroscopy-Principal Components Regression Analyses of Soil Properties . Soil Sci. Soc. Am. J. 65:480-490.
  9. Clark, R. N. 1999. Spectroscopy of rocks and minerals, principles of spectroscopy. PP. 3-58. In: A. N. Rencz (Ed.), Remote Sensing for the Earth Sciences: Manual of Remote Sensing. John Wiley & Sons, New York.
  10. Clark, R., T. King, M. Klejwa, and N.Vergo. 1990. High spectral resolution reflectance spectroscopy of minerals. Geophysical Research, 95:12653-12680.
  11. climate-data for cities worldwide. 2012. Retrieved from http://www.climate-data.org. Germany, Oedheim.
  12. Conforti, M., A. Castrignano, G. Robustelli, F. Scarciglia, M. Stelluti, and G. Buttafuoco. 2015. Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. Catena,124:60-67
  13. Demattê, J., and f. Terra. 2014. A new perspective on evaluation of soils along pedogenetic alterations Geoderma, (217-218):190-200.
  14. Farmer, V., and  J. Russell. 1964. The infrared spectra of layer silicates. Spectrochim. Acta, 20:1149-1173.
  15. Farifteh, J., A. Farshad, and R.J. George .2006. Assessing salt-affected soils using remote sensing, solute modeling, and geophysics. Geoderma, 130(3–4):191 −206.
  16. Gomez, C., P. Lagacherie, and G. Coulouma. 2008. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma,148:141-148.
  17. Gras, J., B. Barthès, B. Mahaut, and S. Trupin. 2014. Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoil. Geoderma, 215:126-134.
  18. Hunt, G. 1980. Spectroscopic properties of rock and minerals. (p. 295). In C. Stewart (Ed.), Handbook of Physical Properties of Rocks. Florida: CRC Press Inc.
  19. Hunt, G., and J. Salisbury. 1971. “Visible and near-infrared spectra of minerals and rocks: II. Carbonates”. Modern Geology. 2:23-30.
  20. Islam, K., B. Singh, and A. McBratney. 2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Aust. J. Soil Res. 41:1101-1114.
  21. Kim, I., R. Pullanagari, M. Deurer, R. Singh, K. hub, and B. Clothier. 2014.The use of visible and near-infrared spectroscopy for the anlysis of soil water repellency.European Journal of Soil Science .65:360-368.
  22. Kuśnierek, K. 2011. Pre-processing of soil visible and near infrared spectra taken in laboratory and field conditions to improve the within-field soil organic carbon multivariate calibration. The Second Global Workshop on Proximal Soil Sensing, Montreal, Canada. 100-103.
  23. Maselli, F., L. Gardin, and L. Bottai. 2006. Automatic mapping of soil texture through the integration of ground, satellite and ancillary data. International Journal of Remote Sensing.TRES-PAP- 427.
  24. Mouazen, A., B. Kuang, J. De Baerdemaeker, and H. Ramon. 2010. Comparison amongprincipal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma,158:23-31.
  25. Nawar, S., H. Buddenbaum, J. Hill, J. Kozak, and A. Mouazen. 2016. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil & Tillage Research.155:510-522.
  26. Nocita, M., A. Stevens, G. Toth, P. Panagos, B. van Wesemael, and L. Montanarella. 2014. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biol. Biochem. 68:337–347.
  27. page, A., R. Miller, and D. Kenney. 1992. Methods of Soil Analysis part II, Chemical and Mineralogical Properties. Madison: SSSA Pub.
  28. Pirie, A., B. Singh, and K. Islam. 2005. Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties. Aust. J. Soil Res. 43: 713-721.
  29. Post, J., and P. Noble. 1993. The near-infrared combination band frequencies of dioctahedral smectites, Micas, and Illites. Clays Clay Min. 41:639-644.
  30. Rinnan, Å., F. van denBerg, and S. Engelsen. 2009. Review of the most common preprocessing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry. 28(10):1201-1222.
  31. Smith, K. 1991. Soil Analysis. 2nd ed., Marcel Decker. New York.
  32. Stenberg, B., R.Viscarra Rossel, A. Mouazen, and J. Wetterlind. 2010. Visible and near infrared spectroscopy in soil science. Advances in Agronomy. 107:163-215.
  33. Stenberg, B., A. Jonsson, and T. Börjesson. 2002. Near infrared technology for soil analysis with implications for precision agriculture. In Near Infrared Spectroscopy: NIR Publications. Proceeding of the 10th International Conference. (pp. 279-284).
  34. Summers, D., M. Lewis, B. Ostendorf, and D. Chittleborough. 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecol. Indic. 11(1):123-131.
  35. Viscarra Rossel, R., and T. Behrens. 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158:46-54.
  36. Viscarra Rossel, R., S.R. Cattle, A. Ortega, and Y. Fouad. 2009. In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma. 150:253-266.
  37. Viscarra Rossel, R., D. Walvoort, A. McBratney, L. Janik, and J. Skjemstad. 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131:59-75.
  38. Xie, X.-L., and  A.-B. Li. 2016. Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy. Biosystems engineering. 152:126-137.
  39. Xie, X.-L., X.-Z. Pan, and B. Sun. 2012. Visible and near-Infrared diffuse reflectance spectroscopy for prediction of soil properties near a copper smelter. Pedosphere. 22:351-366.
  40. Xuemei, L., and L. Jianshe. 2013. Measurement of soil properties using visible and short wave-near infrared spectroscopy and multivariate calibration. Measurement. 46(10):3808-3814.
Statistics
Article View: 1,434
PDF Download: 1,200


counter
Journal management system. designed by sinaweb