Estimating of the Soil Electrical Conductivity by Using EO-1 Hyperion Satellite Images: A case Study in the North of the Uremia plain

Imani, Mina; Bahrami, Hosseinali; Sokouti, Reza; Ghorbannezhad, Fazeze

doi:10.22059/ijswr.2014.51172

Estimating of the Soil Electrical Conductivity by Using EO-1 Hyperion Satellite Images: A case Study in the North of the Uremia plain

Document Type : Research Paper

Authors

¹ MSc Graduated, Faculty of Agriculture, Tarbiat Modares University, Iran

² Associate Professor, Faculty of Agriculture, Tarbiat Modares University, Iran

³ Scientific Member, Research Center of Agriculture and Natural Resources, West Azerbaijan, Iran

⁴ MSc Graduated, GIS, Tarbiat Modares University, Iran

10.22059/ijswr.2014.51172

Abstract

Salinization and alkalization are the most problem in the arid and semi-arid regions, where precipitation is low than evapotranspiration. Under such a climatic condition, soluble salts are accumulated in the soil, which cause lessening of the soil productivity and fertility. So, the identification of the salt affected areas is essential for sustainable soil management. The specific objective of this research is mapping of saline soils by the Hyperion EO-1 satellite images in the Uremia Plain. In this study, spectra responses of the 40 saline soil samples were conducted by the Spectroradiometer Fieldspace 3 and Hyperion image in order to mapping of the soil salinity was acquired from the United State geological survey (USGS) archives. Results indicated a significant correlation (R²= 0.89) between Soil saline Content (SSC) and reflectance percent at the 42 and 219 bands. A soil salinity spectral index (SSI) was constructed from Continuum Removed Reflectance (CR-Reflectance) at 0.762 and 2.345 micrometers. Then, a model for estimation of SSC with SSI was constructed using univariate regression. Model validation yielded a Root Mean Square Error (RMSE) of 1.23 ms/cm and an R²= 0.8. The model was calibrated with a Hyperion reflectance image, on a pixel-by-pixel basis, and reasonable agreements with overall accuracy of 75% and Kappa Coefficient of 0.65. The findings of this project suggest that the satellite hyperspectral data have potential for predicting SSC in this study area.

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