This study explores the possibility of integrating electromagnetic induction, EMI, measurements with hydrological modelling to characterize soil hydraulic behaviour during an infiltration process at the transect scale. A controlled 10-hour irrigation experiment was conducted on bare sandy soil in Italy, where time-lapse apparent electrical conductivity, σ_a, readings were collected along a 24 m transect. Direct soil water content observations were obtained on soil samples at sites spaced 1 m apart and at five depths. Contextually, EMI readings were taken by a CMD Mini Explorer sensor and inverted to estimate bulk electrical conductivity, σ_b, distributions over time, which were subsequently converted to as many water content, θ, distributions through a site-specific θ–σ_b calibration relationship derived from independent TDR measurements taken in the upper 25 cm of soil during the infiltration experiment. Soil hydraulic properties (SHPs) for the two soil horizons of the soil profile were independently measured using the tension infiltrometer method (TIM). Two sets of hydrological simulations were carried out using a dynamic Richards-equation-based model, adopting either auger-measured initial water content profiles with the original SHPs, or EMI-estimated initial water content profiles with SHPs scaled by adjusting the saturated water content. Results show that the EMI-estimated water content distributions can effectively reproduce infiltration dynamics when appropriately scaling initial conditions and SHPs. Within this framework, scaling the SHPs and assigning initial conditions consistent with EMI observations enables the conversion between the high-frequency, microscopic description obtained from point-scale measurements and the low-frequency, macroscopic description provided by EMI monitoring. In the proposed approach, the hydrological model provides a physically based interpretative framework for understanding what an EMI sensor observes during infiltration experiments and allows for reconciling nonlinear temporal evolution of θ distributions as observed by point scale measurements and estimated by EMI during wetting front propagation without the need for empirical scaling relationships. The findings extend the results of Dragonetti et al. (2022), demonstrating that EMI monitoring combined with physically based modelling provides a robust framework for interpreting infiltration processes and estimating SHPs non-invasively at the field scale.