Hydrological models have traditionally been calibrated and evaluated against point-scale observations, such as streamflow, emphasizing the temporal, but not necessarily the spatial component of a model. The main goal of this study is to improve the spatial pattern performance of simulated actual evapotranspiration (AET), a key variable in the land–atmosphere interface, for the entire land-phase of Denmark. This is achieved by integrating fully distributed remote-sensing (RS) data and robust objective functions in the regionalization and optimization of the national water resources model of Denmark (DK-model). For this, monthly spatial patterns of MODIS16 ET at 1 km resolution are used as a reference RS ET estimate in the model calibration. We applied a gradient-based nonlinear Gauss-Marquardt-Levenberg (GML) algorithm for parameter estimation via inverse modeling. The proposed optimization framework includes three calibration strategies: Cal01 represents the traditional calibration approach against discharge and groundwater head observations only, Cal02 expands Cal01 by means of a spatial pattern-oriented objective function, namely the spatial efficiency (SPAEF) metric, targeting ET pattern performance, Cal03 adds to Cal02 an additional parameter regionalization scheme, where the original fixed land use class dependent distribution and seasonal development of leaf area index (LAI), crop coefficient (Kc) and root depth (RD) are replaced with empirical schemes driven by distributed vegetation (NDVI) and soil texture (clay fraction) data. Our results reveal that a significant improvement in the simulated spatial patterns of AET is obtained when combining regionalization schemes and a spatial pattern oriented objective function in Cal03, which is highlighted by a Copula analysis also. At the same time, a very limited trade-off in the model performance of AET patterns, streamflow and groundwater-heads is observed. Finally, the regionalization scheme (Cal03) affected the simulated groundwater recharge according to changes of AET. The findings of this study contribute to further improve spatial representation and evaluation of hydrological models and highlight the need to carefully design regionalization schemes as well as objective functions in fully distributed models.