Allagui et al. (2025) Estimating Deep Soil Salinity by Inverse Modeling of Loop–Loop Frequency Domain Electromagnetic Induction Data in a Semi-Arid Region: Merguellil (Tunisia)

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Land
• Year: 2025
• Date: 2025-12-23
• Authors: Dorsaf Allagui, Julien Guillemoteau, Mohamed Hachicha
• DOI: 10.3390/land15010032

Research Groups

Not explicitly mentioned in the provided text. The study refers to an "in-house laterally constrained inversion (LCI) approach," implying a specific research group developed it, but the group or institution is not named.

Short Summary

This study combines multi-configuration frequency domain electromagnetic induction (FD-EMI) sensors with a laterally constrained inversion (LCI) approach to monitor the vertical distribution of soil salinity in an irrigated area, revealing systematic salinity transfer influenced by irrigation systems and seasonal variations.

Objective

• To monitor the vertical distribution of soil salinity in a salt-affected irrigated area using combined multi-configuration frequency domain electromagnetic induction (FD-EMI) sensors (EM31 and EM38) and a laterally constrained inversion (LCI) approach.

Study Configuration

• Spatial Scale: Field scale (salt-affected irrigated area in Kairouan, central Tunisia), monitoring soil conditions at depths up to 4 meters.
• Temporal Scale: Short and long term, including observations across wet and dry seasons.

Methodology and Data

• Models used: Laterally Constrained Inversion (LCI) approach for inverse modeling of apparent electrical conductivity (ECa) data.
• Data sources: Multi-configuration frequency domain electromagnetic induction (FD-EMI) mono-channel sensors (EM31 and EM38) operated at different heights and with different coil orientations to collect apparent electrical conductivity (ECa) data.

Main Results

• Systematic transfer of salinity from the surface to deeper layers was consistently observed by FD-EMI surveys.
• The intensity and spatial distribution of soil salinity varied significantly depending on the crop type and the frequency and amount of drip or sprinkler irrigation.
• Vertical salinity transfer was also influenced by the prevailing wet or dry season.
• The combined EM31 and EM38 sensors, coupled with the LCI approach, provided a detailed and extensive assessment of soil salinity distribution across spatial and temporal scales at different depths (up to 4 meters) and across various irrigation systems.
• The method successfully imaged the influence of local drip irrigation and evaluated the history of a non-irrigated plot, demonstrating its potential.

Contributions

• Demonstrates the effectiveness of combining two different FD-EMI sensors (EM31 and EM38) with multiple measurement configurations for enhanced depth sensitivity in monitoring soil salinity.
• Introduces and applies a recently developed "in-house" laterally constrained inversion (LCI) approach for deriving detailed spatial and temporal soil salinity information from multi-configuration FD-EMI datasets.
• Provides novel insights into the dynamics of vertical salinity transfer in irrigated agricultural areas, highlighting the influence of different irrigation systems, crop types, and seasonal variations.
• Offers a more extensive and detailed assessment of soil conditions at depths up to 4 meters compared to traditional point-scale soil sampling and laboratory analysis, contributing to sustainable irrigated agricultural production.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Allagui2025Estimating,
  author = {Allagui, Dorsaf and Guillemoteau, Julien and Hachicha, Mohamed},
  title = {Estimating Deep Soil Salinity by Inverse Modeling of Loop–Loop Frequency Domain Electromagnetic Induction Data in a Semi-Arid Region: Merguellil (Tunisia)},
  journal = {Land},
  year = {2025},
  doi = {10.3390/land15010032},
  url = {https://doi.org/10.3390/land15010032}
}