Mapping of Soil Salinity Using Geostatistic and Electromagnetic Induction Methods in Ardakan

Document Type : Research Paper

Authors

1 Department of Soil Science, University College of Agriculture & Natural Resources, University of Tehran

2 Department of Agricultural Machinery, University College of Agriculture & Natural Resources, University of Tehran

3 Academic Member of National Salinity Center

4 Member of National Salinity Center

Abstract

Precise mapping of the spatial distribution of salt-affected soils is prerequisite for effective management of these soils. This study was carried out for mapping soil salinity of 78000 hectares of Ardakan soils (0-30 and 0-100 cm) using 151soil samples which were taken based on hyper cube method. The secondary variables used in co-kriging method were ETM data, terrain analysis and EM38 readings. The best model was selected by means of cross validation and error evaluation methods, such as RMSE and ME methods. Results showed that co-kriging method with EM38 data as a secondary variable was the best method for prediction of soil salinity (69.1, 30.55, 48.8 and 20.41, respectively). Results recommended EM38 as secondary data for mapping soil salinity. Additionally, results showed the largest amount of soil salinity in the north of the area and the smallest values in the areas with more elevation. The concavely shaped plain could help to move soluble salts toward the north of area in which the soils with highest electrical conductivity are found. 

Keywords

References

  1. رحیمیان، م. ح. و هاشمی­نژاد، ی. 1389. واسنجی دستگاه القاءگر الکترومغناطیس برای ارزیابی شوری خاک. مجله پژوهش‌های خاک، علوم خاک و آب، الف، جلد 24، شماره 3، ص. 252-243.
  2. Akramkhanov, A. and Vlek, P.L.G. 2012. The assessment of spatial distribution of soil salinity risk using neural network. Environmental Monitoring and Assessment, 184:2475–2485.
  3. Alison, B.T., Kenneth, J.M., Burras, C.L., Donald, G.B. and Philip M.D. 2005. Improving map accuracy of soil variables using soil electrical conductivity as a covariate. Precision Agriculture, 6: 255–270.
  4. Bouma, J. 1989. Using soil survey data for quantitative land evaluation. Soil Science, 9: 177–213.
  5. Cheraghi S.A.M., Hasheminejhad Y. and Rahimian M.H. 2007. An overview of salinity problem in Iran: assessment and monitoring technology. First Expert Consultation on “Advances in Assessment and Monitoring of Salinization for Managing Salt-affected Habitats” of the Global Network on Salinization Prevention and Productive Use of Salt-affected Habitats (SPUSH) and Meeting on the Status and Progress of Biosaline Agriculture of the Inter-Islamic Network on Biosaline Agriculture. Dubai, UAE.
  6. Corwin, D.L. and Lesch S.M. 2005. Characterizing soil spatial variability with apparent soil electrical conductivity Part II. Case study. Computers and Electronics in Agriculture, 46:135–152.
  7. Douaoui, A.E.K., Nicolas, H. and Walter C. 2006. Detecting Salinity hazard within a semi arid context by means of combining soil and remote – sensing data. Geoderma, 134: 217-230.
  8. Eldeiry, A. and Garcia L.A. 2009. Comparison of regression kriging and cokriging techniques to estimate soil salinity using LANDSAT images. Hydrology Days, 27-37.
  9. Hassani-pak, A. 1998. Geostatistics. Tehran: University of Tehran. In Persian.
  10. Hengl, T., Huvelink, G.B.M. and Stein A. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120: 75–93.
  11. Kinal, J., Stoneman, G.L and Williams M.R. 2006. Calibrating and using an EM31 electromagnetic induction meter to estimate and map soil salinity in the Jarrah and Krri forests of south-western Australia. Forest Ecology and Management, 233:78–84.
  12. Minasny, B. and McBratney A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computer and Geosciences, 32:1378-1388.
  13. Moore, I.D., Grayson, R.B. and Ladson A.R. 1991. Digital terrain modeling: review of hydrological, geomorphological and biological applications. Hydrology Processing, 5:3-30.
  14. Rhoades, J.D. 1976. Measuring, mapping and monitoring field salinity and water depths with soil resistance measurements. FAO Soils Bulletin, 31:69-1 09.
  15. Saey, T., Van Meirvenne, M., Vermeersch, H., Ameloot, N. and Cockx L. 2009. A pedotransfer function to evaluate the soil profile textural heterogeneity using proximally sensed apparent electrical conductivity. Geoderma, 150: 389–395.
  16. Shao, W.H., Ji, Y., Li, P.Y. and You L.B. 2006. Spatial variability of soil nutrients and influencing factors in a vegetable production area of Hebei Province in China. Nutr Cycl Agroecosyst, 75:201–212.
  17. Sheng, J., Ma, L., Jiang, P., Li, B., Huang, F. and Wu, H. 2009. Digital soil mapping to enable classification of the salt-affected soils in desert agro-ecological zones. Agricultural Water Management, 35: 54-60.
  18. Soleimani-damaneh, M. and Zarepisheh, M. 2009. Shannons entropy combining the efficiency results of different DEA models: Method and application, Expert System with Applications, P.5147.
  19. Sparks, D. L., Page, A. L., Helmke, P. A., Leoppert, R. H., Soltanpour, P. N., Tabatabai, M. A., Johnston, G.T. and Summer M.E. 1996. Methods of soil analysis. Madison: Soil Sci. Soc. of America.
  20. Sudduth, K.A., Drummond, S.T. and Kitchen N.R. 2001. Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers and Electronics in Agriculture, 31: 239–264.
  21. Tarr, A.B., Moor, K.J., Bullock, D.G. and Dixon, P.M. 2005. Improving map accuracy of soil variables using soil electrical conductivity as a covariate. Precision Agriculture, 6:255–270.
  22. Triantafilis, J. and Buchanan S.M. 2010. Mapping the spatial distribution of subsurface saline material in the Darling River valley. Journal of Applied Geophysics, 70: 144–160.
  23. Triantafilis, J., Odeh, I.O. A. and McBratney A.B. 2001. Five Geostatistical Models to Predict Soil Salinity from Electromagnetic Induction Data across Irrigated Cotton. Journal of Soil Science Society of America, 65: 869–878.
Iranian Journal of Soil Research
Volume 26, Issue 4
March 2022
Pages 369-380

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