Modeling Soil Salinity along a Hillslope in Iran by Inversion of EM38 Data

Abstract

Electromagnetic (EM) induction has been used to characterize the spatial distribution of salinity. However, most studies have been undertaken to map the areal distribution of the average profile salinity using measurements of the apparent electrical conductivity (ECa, mS m−1). In this study, an EM38 was used to map the distribution of salinity with depth along a 26-km hillslope in central Iran. We generated electromagnetic conductivity images by inverting EM38 ECa data collected at various heights in the EM4Soil software. A number of parameters including forward modeling (cumulative function, CF, and full solution, FS), inversion algorithms (S1 and S2), damping factor (λ), and combinations of different heights were considered to generate calculated soil true electrical conductivity (σ, mS m−1). By comparing different σ against the electrical conductivity of a saturated soil-paste extract (ECe, dS m−1) at various depths, we found that the strongest correlation and smallest modeling error was achieved using the FS, S1, λ = 12, and ECa data collected at 0.4 m alone. We then compared the results achieved by developing a linear regression between σ and ECe at various depths with those achieved using multiple linear regression (MLR) established between ECa and ECe. The inversion method was less time consuming and more robust than the MLR approach. The predicted ECe increased from the crest to the base of the toposequence. The results were consistent with the underlying geology, climate, and local topography. The methodology can be used as guidance for baseline salinity monitoring and management.