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Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions

Author

Listed:
  • Mohamed G. Eltarabily

    (Civil Engineering Department, Faculty of Engineering, Port Said University, Port Said 42523, Egypt
    Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA)

  • Abdulrahman Amer

    (Civil Engineering Department, Faculty of Engineering, Port Said University, Port Said 42523, Egypt)

  • Mohammad Farzamian

    (Instituto Nacional de Investigação Agrária e Veterinária (INIAV), 2780-157 Oeiras, Portugal
    Centre of Geographical Studies (CEG), IGOT, Universidade de Lisboa, 1600-276 Lisbon, Portugal)

  • Fethi Bouksila

    (University of Carthage, National Institute for Research in Rural Engineering, Water, and Forestry (INRGREF), LR Valorization of Non-Conventional Waters (LR 16INRGREF02), BP10, Ariana 2080, Tunisia)

  • Mohamed Elkiki

    (Civil Engineering Department, Faculty of Engineering, Port Said University, Port Said 42523, Egypt
    Civil Engineering Department, Higher Institute for Engineering and Technology, New Damietta 34517, Egypt)

  • Tarek Selim

    (Civil Engineering Department, Faculty of Engineering, Port Said University, Port Said 42523, Egypt)

Abstract

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (EC a ) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (EC e ) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R 2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income.

Suggested Citation

  • Mohamed G. Eltarabily & Abdulrahman Amer & Mohammad Farzamian & Fethi Bouksila & Mohamed Elkiki & Tarek Selim, 2024. "Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions," Land, MDPI, vol. 13(2), pages 1-21, February.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:2:p:225-:d:1337526
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    References listed on IDEAS

    as
    1. Yao, Rongjiang & Yang, Jingsong, 2010. "Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method," Agricultural Water Management, Elsevier, vol. 97(12), pages 1961-1970, November.
    2. Panagiota Antonia Petsetidi & George Kargas, 2023. "Assessment and Mapping of Soil Salinity Using the EM38 and EM38MK2 Sensors: A Focus on the Modeling Approaches," Land, MDPI, vol. 12(10), pages 1-27, October.
    Full references (including those not matched with items on IDEAS)

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