# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 4 Discussion
### ET Model Intercomparison
}}\); mean of observed values (W m\({}^{-2}\)); bias, mean difference between predicted and observed (W m\({}^{-2}\)); MAE, Mean Absolute Error (W m\({}^{-2}\)), RMSE, Root Mean Square Error (W m\({}^{-2}\)); rRMSE, Relative RMSE (–); r, Pearson correlation coefficient (–). TSEB-PT was developed trying to solve some of the issues in sparse vegetation and semi-arid conditions previously raised by less complex models ([PERSON] and [PERSON], 2017), and therefore it adapts better to a wider range of climatic and vegetation conditions ([PERSON] et al., 2018) as it was shown in Tables 5 and 7. METRIC, on the other hand, was primarily designed for standard crops and requires concomitant presence of stressed and well watered-full vegetation conditions within the scene itself. This more often happens in semi-arid climate where irrigated crops and rainfed crops and natural vegetation are present. Those cases in which, either due to the increased presence of clouds (i.e., fewer available pixels in the scene) or in regions where these hot and cold pixels cannot be simultaneously found, METRIC would produce more uncertain retrieval, as already pointed by [PERSON] et al. (2018) and [PERSON] et al. (2019) (in humid or sub-humid areas), or even would not produce any valid data. Similarly, ESVEP was designed and tested in an agricultural area located in a subhumid and monsoon climate ([PERSON] and [PERSON], 2017) and therefore certain assumptions and parameterizations taken in that model might not transfer well to other vegetation or climatic conditions. Despite of TSEB being the model with the largest required amount of input data, this study proposed several new approaches to retrieve some of those inputs operationally, with special focus on exploiting the spectral capabilities of Sentinel-2, in particular the bands in the red-edge region that is sensitive to leaf pigments. A simple empirical approach relating leaf bihemispherical reflectance and transmittance with the leaf biochemical properties resulted in accurate estimates of net radiation. More importantly, due to the larger uncertainty of TSEB models over senescent vegetation, we derived a method to obtain both total LAI and its green fraction based on [PERSON] et al. (2019) FAPAR/FIPAR relationship. Nevertheless, more research is needed to systematically derive other vegetation properties such as canopy height/aerodynamic roughness or vegetation clumping. Finally, it is worth pointing out that even _in situ_ EC measurements are prone to uncertainty as is confirmed for instance by the usual energy imbalance in those systems. Particularly we found a larger disagreement between observed and predicted net radiation in Dahra (see Figures in the Supplement). We hypothesise that this could be due to two possible reasons. Firstly, our modelled irradiance, with depends on TCWV and aerosol optical thickness, could be more noisy at Dahra than the other sites, due to unaccounted dust aerosols in that site placed over the Sahel. The second issue might be the actual \(R_{n}\) measurements, as in this site only a NR-lite (Kipp & Zonen, Netherlands) is available to measure global \(R_{n}\) that might be less accurate than the radiometers at the other sites, which are measuring the four components of radiation. In addition, Harvard Forest site lacks _in situ_ G measurements, which effects the energy balance closure correction. This issue together with the fact that very few cases are available in forests (Table 5), leads us to avoid strong conclusions regarding the performance of the models in forested areas.