Understanding ecologically sensitive wetlands often requires non‐invasive methods to characterize their complex structure (e.g., deposit heterogeneity) and hydrogeological parameters (e.g., porosity and hydraulic conductivity). Here, electrical conductivities of a riparian wetland were obtained using frequency domain electromagnetic induction (EMI) methods. The wetland was previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT) surveys and hence offers an ideal opportunity to objectively assess EMI methods. Firstly, approaches to obtain structural information (e.g., elevation and thickness of alluvium) from EMI data and inverted models were assessed. Regularized and sharp inversion algorithms were investigated for ERT calibrated EMI data. Moreover, the importance of EMI errors in inversion was investigated. The hydrological information content was assessed using correlations with piezometric data and petrophysical models. It was found that EMI data were dominated by the thickness of peaty alluvial soils and relatively insensitive to topography and total alluvial thickness. Furthermore, although error weighting in the inversion improved the accuracy of alluvial soil thickness predictions, the multi‐linear regression method performed the best. For instance, an iso‐conductivity method to estimate the alluvial soil thickness in the regularized models had a normalized mean absolute difference (NMAD) of 21.4%, and although this performed better than the sharp inversion algorithm (NMAD = 65.3%), the multi‐linear regression approach (using 100 intrusive observations) achieved a NMAD = 18.0%. In terms of hydrological information content, correlations between EMI results and piezometric data were poor, however robust relationships between petrophysically derived porosity and hydraulic conductivity were observed for the alluvial soils and gravels. Key Points Raw ECa values are highly correlated with the thickness of alluvial soil in a riparian wetland Alluvial soil thickness predictions from multi‐linear regressions were more accurate than from electromagnetic induction (EMI) inversion methods Robust predictions of hydraulic conductivity across the field site require more extensive intrusive data