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

Accumulation of salts in irrigated soils can be detrimental not only to growing crops but also to groundwater quality. Soil salinity should be regularly monitored, and appropriate irrigation at the required leaching rate should be applied to prevent excessive salt accumulation in the root zone, thereby improving soil fertility and crop production. We combined two frequency domain electromagnetic induction (FD-EMI) mono-channel sensors (EM31 and EM38) and operated them at different heights and with different coil orientations to monitor the vertical distribution of soil salinity in a salt-affected irrigated area in Kairouan (central Tunisia). Multiple measurement heights and coil orientations were used to enhance depth sensitivity and thereby improve salinity predictions from this type of proximal sensor. The resulting multi-configuration FD-EMI datasets were used to derive soil salinity information via inverse modeling with a recently developed in-house laterally constrained inversion (LCI) approach. The collected apparent electrical conductivity (ECa) data were inverted to predict the spatial and temporal distribution of soil salinity. The results highlight several findings about the distribution of salinity in relation to different irrigation systems using brackish water, both in the short and long term. The expected transfer of salinity from the surface to deeper layers was systematically observed by our FD-EMI surveys. However, the intensity and spatial distribution of soil salinity varied between different crops, depending on the frequency and amount of drip or sprinkler irrigation. Furthermore, our results show that vertical salinity transfer is also influenced by the wet or dry season. The study provides insights into the effectiveness of combining two different FD-EMI sensors, EM31 and EM38, for monitoring soil salinity in agricultural areas, thereby contributing to the sustainability of irrigated agricultural production. The inversion approach provides a more detailed representation of soil salinity distribution across spatial and temporal scales at different depths, and across irrigation systems, compared to the classical method based on soil samples and laboratory analysis, which is a point-scale measurement. It provides a more extensive assessment of soil conditions at depths up to 4 m with different irrigation systems. For example, the influence of local drip irrigation was imaged, and the history of a non-irrigated plot was evaluated, confirming the potential of this method.