# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 4 Discussion
### Sharpening and Disaggregation
This is probably due to the scale mismatch between the coarse pixel estimate and the footprint of the EC towers' measurements. We evaluated this by comparing fluxes modelled at Sentinel-2 (i.e., with sharpened \(T_{rad}\)) and Sentinel-3 spatial resolutions against measurements form towers. This was done for all the sites put together and also for the validation sites split into two categories: those in which the tower is located in a landscape feature too small to have significant effect on the original resolution Sentinel-3 \(T_{rad}\) (category \"small\" containing CH, KL, GR and SE sites), and those where the opposite is true (category \"large\" containing SL, DA, HF, HTM, MT TA and KG sites). The results (Table 8) indicate that using sharpened \(T_{rad}\) is most important when modelling H in the \"small\" category. However, the correlation of high-resolution fluxes against tower measurements is in almost all the cases higher than that of low-resolution fluxes and rRMSE is lower or the same in case of turbulent fluxes. Therefore, even though sharpened \(T_{rad}\) might be more prone to errors than actual high-resolution \(T_{rad}\), it is still a good option for downscaling fluxes for model validation [4], addressing therefore the vegetation cover variability within coarse resolution pixels. Nevertheless, there is still an open question on how feasible thermal sharpening is for early detection of water stress at small scales, compared to using high resolution thermal imagery. This issue is especially relevant for precision irrigation tasks and therefore future studies should address this topic. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Feature Size** & **Variable** & **Resolution** & **N** & **Obs.** & **Bias** & **MAE** & **RMSE** & **rRMSE** & **r** \\ \hline \multirow{4}{*}{all} & \multirow{2}{*}{H} & Sentinel-2 & 467 & 178 & \(-\)47 & 65 & 81 & 0.454 & 0.670 \\  & & Sentinel-3 & 456 & 176 & \(-\)38 & 73 & 89 & 0.509 & 0.548 \\ \cline{2-10}  & \multirow{2}{*}{\(\lambda E\)} & Sentinel-2 & 459 & 194 & 22 & 72 & 89 & 0.457 & 0.756 \\  & & Sentinel-3 & 446 & 196 & 13 & 71 & 92 & 0.467 & 0.726 \\ \cline{2-10}  & \multirow{2}{*}{Rn} & Sentinel-2 & 505 & 446 & \(-\)14 & 44 & 56 & 0.125 & 0.908 \\  & & Sentinel-3 & 481 & 446 & \(-\)12 & 42 & 56 & 0.126 & 0.902 \\ \cline{2-10}  & \multirow{2}{*}{G} & Sentinel-2 & 498 & 76 & 14 & 44 & 54 & 0.718 & 0.452 \\  & & Sentinel-3 & 474 & 74 & 13 & 43 & 51 & 0.690 & 0.491 \\ \hline \multirow{4}{*}{small} & \multirow{2}{*}{H} & Sentinel-2 & 85 & 121 & \(-\)29 & 48 & 65 & 0.534 & 0.514 \\  & & Sentinel-3 & 88 & 120 & 4 & 65 & 88 & 0.736 & 0.186 \\ \cline{2-10}  & \multirow{2}{*}{\(\lambda E\)} & Sentinel-2 & 76 & 290 & \(-\)35 & 73 & 88 & 0.303 & 0.706 \\  & & Sentinel-3 & 79 & 290 & \(-\)26 & 70 & 88 & 0.303 & 0.694 \\ \cline{2-10}  & \multirow{2}{*}{Rn} & Sentinel-2 & 87 & 452 & \(-\)27 & 38 & 46 & 0.101 & 0.964 \\  & & Sentinel-3 & 90 & 451 & \(-\)11 & 30 & 37 & 0.083 & 0.970 \\ \cline{2-10}  & \multirow{2}{*}{G} & Sentinel-2 & 87 & 45 & 28 & 47 & 58 & 1.283 & 0.301 \\  & & Sentinel-3 & 90 & 46 & 8 & 45 & 57 & 1.250 & 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Landscape feature size dependence of errors for low and high resolution TSEB-PT modelled fluxes using Decision Trees sharpened temperatures. N, number of valid cases; \(\overline{\text{Obs}}\). ; 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 (-). Figure 2: Error (modelled–measured) distribution of fluxes modelled with TSEB-PT and disTSEB models using sharpened \(T_{rad}\) depending on offset days between a Sentinel-3 \(T_{rad}\) image and the fine-scale Sentinel-2 multispectral image. Error computed for all sites together.