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An adjusted satellite-based model was proposed with the aim of improving spatially distributed evapotranspiration (ET) estimates under plant water stress conditions. Remote sensing data and near surface geophysics information, using electrical resistivity tomography (ERT), were used in a revised version of the original dual crop coefficient (K_c) FAO-56 approach. Sentinel 2-A imagery were used to compute vegetation indices (VI_s) required for spatially estimating ET. The potentiality of the ERT technique was exploited for tracking the soil wetting distribution patterns during and after irrigation phases. The ERT-derived information helped to accurately estimate the wet exposed fraction (f_ew) and therefore the water evaporated from the soil surface into the dual K_c FAO-56 approach. Results, validated by site-specific ET measurements (ET_EC) obtained using the eddy covariance (EC) technique, showed that ERT-adjusted ET estimates (ET_ERT) were considerably reduced (15%) when compared with the original dual K_c FAO-56 approach (ET_FAO), soil evaporation overestimation being the main reason for these discrepancies. Nevertheless, ET_FAO and ET_ERT showed overestimations of 64% and 40% compared to ET_EC. This is because both approaches determine ET under standard conditions without water limitation, whereas EC is able to determine ET even under soil water deficit conditions. From the comparison between ET_EC and ET_ERT, the water stress coefficient was experimentally derived, reaching a mean value for the irrigation season of 0.74. The obtained results highlight how new technologies for soil water status monitoring can be incorporated for improving ET estimations, particularly under drip irrigation conditions.