International audience One of the primary applications of satellite Land Surface Temperature (LST) observations lies in theirutilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals ofmonitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated bySustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex and dynamic process,both temporally and spatially, necessitating LST observations with high spatio-temporal resolution. Presently,none of the existing spaceborne thermal sensors can provide quasi-daily field-scale LST observations, promptingthe development of methods for data fusion (thermal sharpening) of observations from various shortwave andthermal sensors to meet this spatio-temporal requirement. Previous research has demonstrated the effectivenessof combining shortwave-multispectral Sentinel-2 observations with thermal-infrared Sentinel-3 observations toderive daily, field-scale LST and ET estimates. However, these studies also highlighted limitations in capturingthe distinct thermal contrast between cooler LST in irrigated agricultural areas and the hotter, adjacent dryregions. In this study, we aim to address this limitation by incorporating information on thermal spatialvariability observed by Landsat satellites into the data fusion process, without being constrained by infrequentor cloudy Landsat thermal observations and while retaining the longwave radiance emission captured by theSentinel-3 thermal sensor at its native resolution. Two approaches are evaluated, both individually and as acomplementary combination, and validated against in situ LST measurements. The best performing approach,which leads to reduction in root mean square error of up to 1.5 K when compared to previous research,is subsequently used to estimate parcel-level actual evapotranspiration. The ET modeling process has alsoundergone various improvements regarding the gap-filling of input and output data, input datasets and codeimplementation. The resulting ET is validated using lysimeters and eddy covariance towers in Spain, Lebanon,Tunisia, and Senegal resulting in minimal overall bias (systematic underestimation of less than 0.07 mm/day)and a low root mean square error (down to 0.84 mm/day) when using fully global input datasets. The enhancedLST sharpening methodology is sensor agnostic and should remain relevant for the upcoming thermal missionswhile the accuracy of the modeled ET fluxes is encouraging for further utilization of observations from Sentinelsatellites, and other Copernicus data, for monitoring SDG indicator 6.4.1.