Evapotranspiration is a process driven by weather, vegetation, and soil conditions. The complex interrelations among these parameters have been modelled by numerous remote-sensing energy balance algorithms. When estimating evapotranspiration on a regional scale, the spatial variability of the weather parameters is important and thus closer attention to the meteorological input data is required. The aim of this work is to improve the accuracy of estimating actual evapotranspiration by integrating outputs from a meteorological model into a remotely sensed energy balance model. In order to achieve this, a time series of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were processed to retrieve daily evapotranspiration values using raster meteorological data. The ITA-MyWater tool implementing the ReSET-Raster algorithm was used in the Tâmega trans-boundary watershed shared by Portugal and Spain. The results were compared to the global MODIS evapotranspiration products for validation, achieving a coefficient of correlation of 0.61 and a root mean square error of 0.92 mm day^–1. Compared with an actual evapotranspiration map that was generated using weather station data, there were improvements in the spatial distribution, especially in dry areas where differences between evapotranspiration estimations of up to 1.88 mm day^–1 were noticed. The proposed methodology contributes to the improved estimation of water use, an important parameter of water cycles, using satellite remote-sensing data.

To increase water productivity and assess water footprints in irrigated systems, there is a need to develop cheap and readily available estimates of components of water balance at fine spatial scales. Recent developments in satellite remote sensing platforms and modelling capacities have opened opportunities to address this need, such as those being developed in the WaterSENSE project. This paper showed how evapotranspiration, soil moisture, and farm-dam water volumes can be quantified based on the Copernicus data from the Sentinel satellite constellation. This highlights distinct differences between energy balance and crop factor approaches and estimates that can be derived from the point scale to the landscape scale. Differences in the results are related to assumptions in deriving evapotranspiration from remote sensing data. Advances in different parts of the water cycle and opportunities for crop detection and yield forecasting mean that crop water productivity can be quantified at field to landscape scales, but uncertainties are highly dependent on input data availability and reference validation data.