Quantitative mapping of soil salinity using the DUALEM‐21S instrument and EM inversion software

To generate baseline data for the purpose of monitoring the efficacy of remediation of a degraded landscape, we demonstrate a method for 3‐dimensional mapping of electrical conductivity of saturated soil paste extract (EC e ) across a study field in central Haryana, India. This is achieved by establishing a linear relationship between calculated true electrical conductivity (σ) and laboratory measured EC e at various depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m). We estimate σ by inverting DUALEM‐21S apparent electrical conductivity (EC a ) data using a quasi‐3‐dimensional inversion algorithm (EM4Soil‐V302). The best linear relationship (EC e = −11.814 + 0.043 × σ) was achieved using full solution (FS), S1 inversion algorithm, and a damping factor (λ) of 0.6 that had a large coefficient of determination ( R 2 = 0.84). A cross‐validation technique was used to validate the model, and given the high accuracy (RMSE = 8.31 dS m −1 ), small bias (mean error = −0.0628 dS m −1 ), large R 2 = 0.82, and Lin's concordance (0.93), between measured and predicted EC e , we were well able to predict the EC e distribution at all the four depths. However, the predictions made in the topsoil (0–0.3 m) at a few locations were poor due to limited data availability in areas where EC a changed rapidly. In this regard, improvements in prediction can be achieved by collection of EC a in more closely spaced transects, particularly in areas where EC a varies over short spatial scales. Also, equivalent results can be achieved using smaller combinations of EC a data (i.e., DAULEM‐1S, DUALEM‐2S), although with some loss in precision, bias, and concordance.