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Remote sensing and GIS-based modeling for predicting soil salinity at the watershed scale in a semi-arid region of southern Iran

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Abstract

Soil salinization is considered one of the main causes of land degradation and environmental hazards, especially in semi-arid regions such as Iran. Modelling soil salinity is becoming an efficient technique in environmental hazard analysis at both local and global scales. In this work, we predict soil salinity in the Khuzestan province, Iran, by using remote sensing (RS) and Geographical Information Systems (GIS) techniques. We collected soil salinity measurements at 108 points, at locations determined by Latin hypercube sampling, and at a depth of 0–30 cm. Vegetation and soil salinity spectral indices, extracted from Landsat-8 OLI (on June 11, 2021) and Sentinel-2 MSI (on June 5, 2021) satellite imagery, were used as input variables for predicting soil salinity. The satellite images and field measurements data were measured on the same date. A step-wise regression model was applied to predict soil salinity from the spectral indices. Our results showed that soil salinity in the study area ranged from 0.85 to 120.0 dS m−1. The best-performing regression model for the estimation of soil salinity was found to use a combination of vegetation and soil salinity spectral indices from both Landsat-8 OLI and Sentinel-2 MSI data. The statistical analysis showed that the results obtained by spectral indices extracted from Sentinel-2 MSI data were better than those extracted from Landsat-8 OLI data. The best-performing regression model was selected based on the highest R2 (0.862) and RPD (3.301) and lowest RMSE (9.19 dS m−1) using a validation dataset.

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Authors and Affiliations

  1. Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar, Iran

    Mohammad Khajehzadeh

  2. Department of Natural Resource and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz, Iran

    Sayed Fakhreddin Afzali

  3. Department of Natural Engineering, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran

    Afshin Honarbakhsh

  4. Facultad de Ingeniería, Universidad de Talca, Talca, Chile

    Ben Ingram

Authors
  1. Mohammad Khajehzadeh
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  2. Sayed Fakhreddin Afzali
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  3. Afshin Honarbakhsh
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  4. Ben Ingram
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Contributions

Conceptualization: Mohammad Khajehzadeh; methodology: Mohammad Khajehzadeh and Afshin Honarbakhsh; formal analysis and investigation: Mohammad Khajehzadeh, Afshin Honarbakhsh, and Sayed Fakhreddin Afzali; writing—original draft preparation: Mohammad Khajehzadeh; writing—review and editing: Mohammad Khajehzadeh, Sayed Fakhreddin Afzali, Afshin Honarbakhsh, and Ben Ingram; resources: Mohammad Khajehzadeh; Supervision: Mohammad Khajehzadeh and Afshin Honarbakhsh.

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Correspondence to Mohammad Khajehzadeh or Sayed Fakhreddin Afzali.

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Khajehzadeh, M., Afzali, S.F., Honarbakhsh, A. et al. Remote sensing and GIS-based modeling for predicting soil salinity at the watershed scale in a semi-arid region of southern Iran. Arab J Geosci 15, 423 (2022). https://doi.org/10.1007/s12517-022-09762-4

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  • DOI: https://doi.org/10.1007/s12517-022-09762-4

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