Deriving and Assessing Spectrotransfer Function and Pedotransfer Function in Predicting Soil Cation Exchange Capacity

Authors

1 Former MSc. Student, Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Assistant Professor., Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

3 PhD., Soil, Water and Environmental Science Department, University of Arizona, USA

Abstract

Cation exchange capacity (CEC) is an important soil physicochemical property that has great effect on fertility and soil quality management. Measurement of CEC is difficult, time-consuming and expensive. The objective of this study was to assess whether inclusion of soil spectral data as a unique set of the predictors and alternative to soil basic properties would improve CEC predictions. Consequently, a total of 120 soil samples were collected from surface soil layer. The CEC and easily-determined soil properties were measured by standard laboratory methods. The spectral reflectance of soils over 350 to 2500 nm range were also determined using a handheld spectroradiometer apparatus. Different pre-processing techniques were evaluated after recording the spectra. Stepwise multiple linear regression (SMLR) was used to estimate some soil properties and CEC. Three scenarios including spectrotransfer functions (STF), pedotransfer functions (PTF) and spectropedotransfer functions (SPTF) were investigated. Results showed that STF had higher accuracy (RPD=1.50; RMSE=2.57 cmolc/kg) than the others in predicting soil CEC. PTF (RPD=1.09; RMSE=3.55 cmolc/kg) and SPTF (RPD=0.95; RMSR=4.06 cmolc/kg) provided poor predictions accuracy. These results suggest the efficacy of the spectral data, which can be used as an indirect, simple, and fast method to predict soil cation exchange capacity.

Keywords

References

  1. تقی­زاده­مهرجردی، ر. ا.، سرمدیان، ف.، ذوالفقاری، ع. ا.، جعفری، ا. 1394. پیش­بینی ظرفیت تبادل کاتیونی خاک­های ایران با استفاده از روش­های گوناگون. مهندسی زراعی (مجله علمی کشاورزی)، 1(38): 77-59.
  2. خداوردی­لو، ح.، حسینی عربلو، ن. آ. 1393. ایجاد، ارزیابی و مقایسه توابع انتقالی کلاسی و پیوسته برای برآورد ظرفیت تبادل کاتیونی خاک در چند کلاس بافتی. مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، 18(67): 320-311.
  3. معلمی، س.، دوات‌گر، ن. 1390. مقایسه توابع انتقالی رگرسیونی و شبکه عصبی مصنوعی در برآورد گنجایش تبادل کاتیونی خاک‌های گیلان. مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، 15(55): 181-169.
  4. مهاجر، ر.، صالحی، و. ن.، بیگی هرچگانی، ح. 1388. تخمین ظرفیت تبادل کاتیونی خاک با استفاده از رگرسیون و شبکه عصبی و اثر تفکیک داده­ها بر دقت و صحت توابع. علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی)، 49: 97-83.
  5. میرخانی، ر.، شعبانپور، م.، سعادت، س. ۱۳۸۴. محاسبه ظرفیت تبادل کاتیونی خاک با استفاده از بافت خاک و درصد ماده آلی در خاک های استان گلستان، مجله علوم آب و خاک، 19(2): 235-242.
  6. هزارجریبی، ا.، نصرتی کاریزک، ف.، عبداله­نژاد، ک.، قربانی، خ. 1392. بررسی امکان پیش­بینی ظرفیت تبادل کاتیونی خاک با استفاده از پارامترهای زودیافت. نشریه آب و خاک (علوم و صنایع کشاورزی)، 27(4): 719-712.
  7. Amini M., K.C. Abbaspour, H. Khademi, N. Fathianpour, M. Afyuni, and R. Schulin. 2005. Neural Network models to predict cation exchange capacity in arid regions of Iran. Europ. J. Soil Sci. 56: 551-559.
  8. Babaeian, E., M. Homaee, C. Montzka, H. Vereecken, and A. A. Norouzi. 2015a. Towards retrieving soil hydraulic properties by hyperspectral remote sensing. Vadoze Zone J. 14(3): doi:10.2136/vzj2014.07.0080.
  9. Babaeian, E., M. Homaee, H. Vereecken, C. Montzka, A. A. Norouzi, and M. T. van Genuchten, 2015b. A Comparative Study of Multiple Approaches for Predicting the Soil–Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties. Soil Sci. Soc. Am. J. 79: 1043-1058.
  10. Bell, M. A. and H. van Keulen. 1995. Soil pedotransfer functions for four Mexican soils. Soil Sci. Soc. Am. J. 59: 865–871.
  11. Bilgili, A. V., H. M. Van Es, F. Akbas, A. Durak, and W. D. Hively. 2010. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. J. Arid Environ. 74(2): 229-238.
  12. Chang, C.W. and D.A. Laird. 2002. Near-infrared reflectance spectroscopic analysis of soil C and N. Soil Sci. 167(2): 110-116.
  13. Clark, R.N., T.V.V. King, M. Klejwa, G.A. Swayze, and N. Vergo. High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. B: Solid Earth, 1990, 95.B8: 12653-12680.
  14. Feyziyev, F., M. Babayev, S. Priori, and L. Giovanni. (2016). Using Visible-near infrared spectroscopy to predict soil properties of Mugan plain, Azerbaijan. J. Soil Sci. 6: 52-58
  15. 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(2): 141-148.
  16. Hepper, E. N., D. E. Buschiazzo, G.G. and A. Hevia. Urioste, and L. Anton. 2006. Clay mineralogy, cation exchange capacity and specific surface area of loess soils with different volcanic ash contents. Geoderma, 135: 216-223.
  17. Islam, K., B. Singh, and A. B. McBratney. 2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy.Soil Res. 41(6): 1101-1114.
  18. Janik, L.J., S.T. Forrester, and A. Rawson. 2009. The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis. Chemometr Intell Lab Sys. 97(2): 179-188.
  19. Keller A., B. Von Steiger, S.T. Van der Zee, and R. Schuline. 2001. A stochastic empirical model for regional heavy metal balances in agroecosystems. J. of Environ Qual. 30:1976-1989.
  20. Klute, A. 1986. Methods of soil analysis. Part 1: Physical and mineralogical methods (No. Ed. 2). American Society of Agronomy, Inc. Soil Science Society of America, Madison, Wisconsin, 1358.
  21. Krogh, L., H. Breuning-Madsen, and M. H. Greve. 2000. Cation exchange capacity pedotransfer function for Danish Manrique, L. A., Jones, C. A. and Dyke, P. T. 1991. Predicting cation exchange capacity from soil physical and chemical properties. Soil Sci. Soc. Am. J. 50:787-794.
  22. Manrique, L.A., C.A. Jones, and P. T. Dyke. 1991. Predicting cation-exchange capacity from soil physical and chemical properties. Soil Sci. Soc. Am. J. 55(3): 787-794.
  23. McBratney A.B., B. Minasny, S. R. Cattle, and R. W. Vervoort. 2002. From pedotransfer function to soil inference systems. Geoderma, 93:225-253.
  24. Page, A. L., R. H. Miller, and D. R. Keeney. 1982. Methods of soil analysis. Part 2. Chemical and microbiological properties. American Society of Agronomy, Inc. Soil Science Society of America, Madison, Wisconsin, 1159.
  25. Savvides, A., R. Corstanje, S. J. Baxter, B. J. Rawlins, and R.M. Lark. 2010. The relationship between diffuse spectral reflectance of the soil and its cation exchange capacity is scale-dependent. Geoderma, 154(3): 353-358.
  26. Seybold, C. A., R. B. Grossman and T. G. Reinsch. 2005. Preicting Cation Exchange Capacity for Soil Survey Using Linear Models. Soil Sci. Soc. Am. J. 69:856-86.
  27. Shepherd, K.D., and M. G. Walsh. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 66(3): 988-998.
  28. Shirazi, M.A., and L. Boersma. 1984. A unifying quantitative analysis of soil texture. Soil Sci. Soc. Am. J. 48: 142-147.
  29. Stenberg, B., R. A. V. Rossel, A. M. Mouazen, and J. Wetterlind, 2010. Chapter five-visible and near infrared spectroscopy in soil science. Adv. Agron. 107: 163-215.
  30. Vereecken, H., M. Weynants, M. Javaux, Y. Pachepsky, M. G. Schaap, and M. Th. van Genuchten. 2010. Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review. Vadose Zone J. 9(4): 795-820.
  31. Viscarra Rossel, R., R. McGlynn, A. McBratney. 2006. Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma, 137(1): 70-82.
  32. Willmott, C.J. (1981). On the validation of models. Physical Geogr. 2: 184–194.
Iranian Journal of Soil Research
Volume 31, Issue 4
March 2018
Pages 641-653

Files

Share

How to cite

Statistics