# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 3 Results
In grassland and cropland TSEB-PT and disTSEB produce very similar fluxes while in savanna disTSEB improves the accuracy of modelled H and \(\lambda E\) by up to 10 W m\({}^{-2}\). METRIC has its best overall performance in \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **Model** & **N** & **Obs.** & **Bias** & **MAE** & **RMSE** & **rRMSE** & **r** \\ \hline \multirow{4}{*}{H} & METRIC & 450 & 231 & \(-6\) & 115 & 164 & 0.709 & 0.151 \\  & TSEB-PT & 467 & 235 & \(-103\) & 118 & 145 & 0.618 & 0.417 \\  & disTSEB & 452 & 233 & \(-93\) & 111 & 138 & 0.592 & 0.422 \\  & ESVEP & 386 & 225 & 22 & 157 & 219 & 0.972 & 0.254 \\ \hline \multirow{4}{*}{\(\lambda E\)} & METRIC & 417 & 141 & 31 & 92 & 125 & 0.885 & 0.477 \\  & TSEB-PT & 459 & 134 & 82 & 94 & 124 & 0.919 & 0.698 \\ \cline{1-1}  & disTSEB & 442 & 137 & 83 & 95 & 125 & 0.916 & 0.699 \\ \cline{1-1}  & ESVEP & 326 & 158 & 12 & 102 & 134 & 0.847 & 0.388 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Error metrics for METRIC, TSEB-PT, disTSEB (TSEB-PT with flux disaggregation) and ESVEP modelled fluxes using Decision Tree sharpened temperatures and closing the energy balance gap in field measurements by preserving the Bowen ratio. N, number of valid cases; 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 (\(-\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **Model** & **N** & **Obs.** & **Bias** & **MAE** & **RMSE** & **rRMSE** & **r** \\ \hline \multirow{4}{*}{H} & METRIC & 450 & 177 & 49 & 103 & 156 & 0.885 & 0.238 \\  & TSEB-PT & 467 & 178 & \(-47\) & 65 & 81 & 0.454 & 0.670 \\  & disTSEB & 452 & 178 & \(-38\) & 62 & 76 & 0.429 & 0.671 \\  & ESVEP & 386 & 166 & 81 & 140 & 220 & 1.323 & 0.380 \\ \hline \multirow{4}{*}{\(\lambda E\)} & METRIC & 417 & 201 & \(-29\) & 100 & 128 & 0.637 & 0.472 \\  & TSEB-PT & 459 & 194 & 22 & 72 & 89 & 0.457 & 0.756 \\  & disTSEB & 442 & 196 & 24 & 72 & 88 & 0.451 & 0.769 \\  & ESVEP & 326 & 221 & \(-51\) & 111 & 140 & 0.635 & 0.420 \\ \hline \multirow{4}{*}{Rn} & METRIC & 505 & 446 & \(-10\) & 39 & 51 & 0.113 & 0.920 \\  & TSEB-PT & 505 & 446 & \(-14\) & 44 & 56 & 0.125 & 0.908 \\ \cline{1-1}  & disTSEB & 480 & 449 & \(-10\) & 40 & 49 & 0.110 & 0.927 \\ \cline{1-1}  & ESVEP & 496 & 446 & \(-24\) & 46 & 59 & 0.132 & 0.907 \\ \hline \multirow{4}{*}{G} & METRIC & 498 & 76 & \(-1\) & 40 & 50 & 0.668 & 0.497 \\ \cline{1-1}  & TSEB-PT & 498 & 76 & 14 & 44 & 54 & 0.718 & 0.452 \\ \cline{1-1}  & disTSEB & 473 & 77 & 3 & 41 & 50 & 0.657 & 0.505 \\ \cline{1-1}  & ESVEP & 491 & 76 & 22 & 47 & 60 & 0.790 & 0.410 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error metrics for METRIC, TSEB-PT, disTSEB (TSEB-PT with flux disaggregation) and ESVEP modelled fluxes using Decision Tree sharpened temperatures and closing the energy balance gap in field measurements by assigning residual energy to latent heat flux. N, number of valid cases; 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 (\(-\)). savanna (RMSE of 132 W m\({}^{-2}\) and r of 0.43 for H; RMSE of 99 W m\({}^{-2}\) and r of 0.61 for \(\lambda E\)) followed by cropland while ESVEP produces inaccurate H in all vegetation types (rRMSE \(>\) 1) and its best overall \(\lambda E\) in grassland. It should also be noted that RMSE of \(R_{n}\) is for all models double in savanna (\(\approx\)65 W m\({}^{-2}\)) than in the other land cover types. This is due to vegetation being most sparse at those sites meaning that uncertainties in estimation of albedo and emissivity of soil have the biggest influence on shortwave and longwave net radiation respectively.