Land evaporation at small spatial scales has been well characterized in the past due to its importance for hydrology, agriculture and meteorology. Our more recent understanding of its relevance for climate processes has driven a demand for evaporation data sets at large scales. However, the production of these data records remains challenging. The limitations inherent in directly measuring evaporative fluxes from space means that remote sensing observations of related variables need to be combined within models. Evaluating the largescale performance of these evaporation models is critical to guide future efforts in our goal to achieve a better understanding of the Earth energy and water cycles. The main objective of SMOS+ET has been to investigate the potential advantages of using SMOS soil moisture and vegetation optical depth (VOD) to reduce uncertainty in satellite-based evaporation estimates. With this goal, GLEAM (Global Land Evaporation: the Amsterdam Methodology, Miralles et al. 2011) has been applied to simulate evaporation fields over continental Australia. Model estimates of terrestrial evaporation and root-zone soil moisture have been validated using an in-situ network of eddy-covariance towers and soil moisture probes. Results from this project indicate that the assimilation of SMOS soil moisture has potential for improving the soil moisture and evaporation estimates of GLEAM. Both ascending and descending SMOS soil moisture have a positive impact on the model simulations. On the other hand, results show that the quality of the ascending AMSR-E soil moisture is too low to improve the model simulations at the study sites that were considered. While the descending soil moisture retrievals from AMSR-E have a sufficiently high quality to improve the model simulations, the positive impact is approximately 5% lower than when SMOS soil moisture is assimilated.