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To implement management plans, the salt content across affected fields and with depth needs mapping. In this study, we developed a method to map the distribution of normal, uniformly saline, and inverted salinity profiles. We did this by establishing a linear regression (LR) between calculated true electrical conductivity (σ) and electrical conductivity of the saturated soil-paste extract (ECe). We estimated σ by inverting the apparent electrical conductivity (ECa) collected from a DUALEM-421. The ECa values were collected along 13 parallel transects spaced 50 m apart. The inversion was performed using a quasi-three-dimensional model available in the EM4Soil software, where we chose the full solution and S1 inversion algorithm with a damping factor (λ) of 0.3. Using cross-validation, the quasi-three-dimensional model yielded a high accuracy (RMSE = 5.28 dS m−1), small bias (mean error ME = −0.03 dS m−1), and large R2 (0.88) and Lin's concordance (0.93). While slightly better results were achieved using individual LRs established at each depth increment overall (i.e., RMSE = 4.35 dS m−1, ME = −0.17 dS m−1, R2 = 0.92, and Lin's concordance = 0.96) and with the DUALEM-421 ECa, the inversion approach requires the development of a single LR (i.e., ECe = 4.1253 + 0.0167σ), which enables efficiencies in estimating salinity and allows ECe to be estimated at any depth where σ was estimated within a three-dimensional electromagnetic conductivity image. This can improve understanding of the cause and best management of salinity. Improvements in accuracy and bias can be achieved by collection of ECa on more closely spaced transects.
Zare et al. (Sun,) studied this question.