# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 5 Conclusions  
The multispectral shortwave images acquired by the Sentinel-2 satellites at 10-20 m spatial resolution are highly suitable for characterizing vegetation in order to derive inputs required for evapotranspiration models. At the same time, thermal data acquired daily by Sentinel-3 satellites is also suitable as input to the ET models. However, its low spatial resolution (1 km) needs to be increased if the models are to be run at the scale of predominant landscape features, which is usually on the order of tens of meters. This study evaluated three thermal-based remote sensing ET models (METRIC, ESVEP and TSEB-PT) and a thermal sharpening method (ensembles of modified Decision Trees) in order to derive methodology for operational estimates of water and energy fluxes using Sentinel data and applicable for the whole globe. Further evolution of the thermal sharpening methodology by using other data sources with high spatial resolution and variable temporal resolutions, e.g., Sentinel-1 radar [95] or Landsat thermal observations [96], is planned. TSEB-PT produced overall the most accurate estimates in terms of sensible heat and latent heat (i.e., evapotranspiration) fluxes, being robust in different land covers and climates. Additional disaggregation step further improved TSEB-PT output accuracy in savanna ecosystems. Without any site-specific tuning and relying only on global datasets the methodology achieved RMSE of 80-90 W m\({}^{-2}\) for modelled instantaneous \(H\) and \(\lambda E\) across eleven validation sites located in different land cover classes and climatic conditions. In an agricultural setting the modelled fluxes were more accurate with rRMSE of \(\lambda E\) of around 0.3 which is of the same magnitude as uncertainty of the measured turbulent fluxes from the validation dataset. Until a new generation of thermal satellites are launched [97], the proposed methodology will be useful solution for overcoming the lack of thermal data with high spatio-temporal resolution required for operational ET modelling at field scale. **Supplementary Materials:** The following are available at [[http://www.mdpi.com/2072-4292/12/9/1433/s1](http://www.mdpi.com/2072-4292/12/9/1433/s1)]([http://www.mdpi.com/2072-4292/12/9/1433/s1](http://www.mdpi.com/2072-4292/12/9/1433/s1)), Figure S1: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Sierra Loma, Figure S2: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Choptank, Figure S3: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Dahra, Figure S4: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Grillenburg, Figure S5: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Harvard, Figure S6: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Hyltemossa, Figure S7: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Klingenberg, Figure S8: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Maidadas, Figure S9: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Salthausen, Figure S10: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Taous, Figure S11: Model scatterplot of predicted vs. EC observed using sharpened Trad with Decision Trees for Kendall Grassland. **Author Contributions:** Conceptualization, R.G. and H.N. ; Data curation, R.G., H.N., LS. and G.K.; Formal analysis, R.G., H.N., LS. and G.K.; Investigation, R.G. and H.N. ; Methodology, R.G. and H.N. ; Software, R.G. and  
Figure 3: Scatterplot between the input ERA5 ensemble meant reanalysis data and in situ measurements for the main atmospheric forcings. Data from all validation sites is shown on the same plot. H.N. ; Writing--original draft, R.G. and H.N. ; Writing--review & editing, I.S.