Abstract To understand the limitations of saline soil and determine best management practices, simple methods need to be developed to determine the salinity distribution in a soil profile and map this variation across the landscape. Using a field study in southwestern Australia, we describe a method to map this distribution in three dimensions using a DUALEM ‐1 instrument and the EM 4Soil inversion software. We identified suitable parameters to invert the apparent electrical conductivity ( EC a – mS /m) data acquired with a DUALEM ‐1, by comparing the estimates of true electrical conductivity ( σ – mS /m) derived from electromagnetic conductivity images ( EMCI ) to values of soil electrical conductivity of a soil‐paste extract ( EC e ) which exhibited large ranges at 0–0.25 (32.4 dS /m), 0.25–0.50 (18.6 dS /m) and 0.50–0.75 m (17.6 dS /m). We developed EMCI using EM 4Soil and the quasi‐3d (q‐3d), cumulative function ( CF ) forward modelling and S2 inversion algorithm with a damping factor ( λ ) of 0.07. Using a cross‐validation approach, where we removed one in 15 of the calibration locations and predicted EC e , the prediction was shown to have high accuracy ( RMSE = 2.24 dS /m), small bias ( ME = −0.03 dS /m) and large Lin's concordance (0.94). The results were similar to those from linear regression models between EC a and EC e for each depth of interest but were slightly less accurate (2.26 dS /m). We conclude that the q‐3d inversion was more efficient and allowed for estimates of EC e to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.