# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 3 Results  
The overall performance of the tested models using sharpened temperatures from Decision Trees regressor (hereinafter \(T_{rad,DT}\)) is shown in Table 3. Scatter plots of modelled versus measured fluxes for all the validation sites are in the Supplement. We removed all the cases in which the S3 image was contaminated by clouds in the vicinity of the flux towers or in which the SLSTR view zenith angle was larger than 45 degrees. In addition, we filtered all cases where estimated \(R_{n}\leq\) 50 W m\({}^{-2}\), assuming that noisy outputs will be produced under low available energy, as well as those yielding unrealistic fluxes during daytime (\(\leq\)\(-\)500 W m\({}^{-2}\) and \(\geq\)1000 W m\({}^{-2}\)). After filtering the data, more than 400 cases were available overall for the following analyses. However, it is worth noting that ESVEP yielded significantly fewer valid retrievals. This issue might be due to the fact that ESVEP's end-member estimation equations were designed and parametrised for herbaceous crops [44] while in this study they were applied to varied land-covers. All models returned a similar performance regarding the estimation of \(R_{n}\), with mean bias between \(-\)10 and \(-\)24 W m\({}^{-2}\), RMSE ranging between 49 and 59 W m\({}^{-2}\) and \(r\) above 0.91. This similar behaviour is explained by the fact that all models share the same approach and same inputs in modelling net shortwave radiation, which is the component with larger magnitude of \(R_{n}\). Likewise, G showed similar behaviour as well, but in this case \(G_{METRIC}\) is computed differently as it is a function of surface \(R_{n}\)[31; 32] as opposed to TSEB and ESVEP where, as two-source models, G is computed from \(R_{n,S}\)[36; 44]. The main differences in model performance are therefore in the estimation of turbulent fluxes (i.e., sensible and latent heat fluxes), and TSEB (TSEB-PT and disTSEB) usually produced most accurate estimates in terms of RMSE (\(\approx\)80 W m\({}^{-2}\), 45% relative error, in H; and \(\approx\)90 W m\({}^{-2}\), 45% relative error, in \(\lambda E\)) and higher correlation between observed and predicted values (\(\approx\)0.67 for H and \(\approx\)0.76 for \(\lambda E\)). disTSEB performs slightly better than TSEB-PT but the difference is not significant. For METRIC and ESVEP, the RMSE values are in all cases higher than 120 W m\({}^{-2}\) (going as high as 220 W m\({}^{-2}\) in case of H modelled with ESVEP) and with lower correlation (\(\leq\)0.47). The choice of closing the energy balance gap in field measurements by assigning it to \(\lambda E\) has influence on the above results. Therefore, in Table 4 we also present the accuracy statistics of the turbulent fluxes when Bowen ratio is preserved during the energy gap closure procedure. The overall ranking of the models is preserved with the TSEB models still obtaining the lowest RMSE and highest correlation coefficients. However, the differences between the models (particularly in case of RMSE) are not as large as in Table 3. In particular the RMSE of the TSEB models increases significantly while there is a decrease in \(r\), while the influence of closure method on the other two models is much weaker with the RMSE of ESVEP even decreasing slightly. In subsequent analysis we always assign the residual energy to \(\lambda E\). In order to evaluate the model sensitivity and uncertainty to different vegetation types, we have split the results of Table 3 into four main vegetation types, depending on differences in aerodynamic roughness, horizontal homogeneity and/or seasonal dynamics/senescence (i.e., croplands, grasslands, savannas and forests, Table 5). Similar to the overall results, the TSEB models output most accurate turbulent fluxes across all four vegetation types. They obtain the best results for H in grassland (RMSE \(\approx\) 70 W m\({}^{-2}\), r \(\approx\) 0.8) and for \(\lambda E\) in cropland (RMSE \(\approx\) 80 W m\({}^{-2}\), r \(\approx\) 0.75).