# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 3 Results
Finally, very few valid cases are available to evaluate the forest sites and hence the results are not very conclusive, with the TSEB models again outperforming the METRIC and ESVEP. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Variable** & **Land Cover** & **Model** & **N** & **Obs.** & **Bias** & **MAE** & **RMSE** & **rRMSE** & **r** \\ \hline \multirow{8}{*}{H} & \multirow{4}{*}{cropland} & METRIC & 187 & 158 & 61 & 92 & 115 & 0.726 & 0.285 \\  & & TSEB-PT & 189 & 158 & \(-\)57 & 71 & 86 & 0.546 & 0.503 \\  & & disTSEB & 177 & 157 & \(-\)48 & 68 & 83 & 0.532 & 0.440 \\  & & ESVEP & 166 & 147 & 78 & 125 & 190 & 1.292 & 0.309 \\ \cline{2-10}  & \multirow{4}{*}{grassland} & METRIC & 103 & 195 & 18 & 132 & 204 & 1.050 & 0.164 \\  & & TSEB-PT & 110 & 197 & \(-\)24 & 58 & 73 & 0.369 & 0.788 \\  & & disTSEB & 108 & 196 & \(-\)26 & 58 & 71 & 0.365 & 0.792 \\  & & ESVEP & 92 & 185 & 50 & 120 & 198 & 1.070 & 0.437 \\ \cline{2-10}  & \multirow{4}{*}{savanna} & METRIC & 148 & 189 & 37 & 83 & 132 & 0.700 & 0.425 \\  & & TSEB-PT & 151 & 187 & \(-\)50 & 62 & 78 & 0.416 & 0.671 \\  & & disTSEB & 150 & 187 & \(-\)35 & 55 & 68 & 0.364 & 0.701 \\  & & ESVEP & 114 & 176 & 107 & 177 & 275 & 1.564 & 0.364 \\ \cline{2-10}  & \multirow{4}{*}{forest} & METRIC & 12 & 160 & 260 & 278 & 370 & 2.306 & \(-\)0.030 \\  & & TSEB-PT & 17 & 202 & \(-\)45 & 74 & 98 & 0.485 & 0.661 \\  & & disTSEB & 17 & 202 & \(-\)35 & 76 & 94 & 0.468 & 0.657 \\  & & ESVEP & 14 & 193 & 110 & 156 & 184 & 0.953 & 0.692 \\ \hline \multirow{8}{*}{\(\lambda E\)} & \multirow{4}{*}{cropland} & METRIC & 179 & 256 & \(-\)80 & 105 & 135 & 0.527 & 0.550 \\  & & TSEB-PT & 183 & 254 & 11 & 67 & 82 & 0.322 & 0.748 \\  & & disTSEB & 169 & 261 & 13 & 69 & 83 & 0.320 & 0.738 \\  & & ESVEP & 145 & 269 & \(-\)83 & 111 & 142 & 0.527 & 0.525 \\ \cline{2-10}  & \multirow{4}{*}{grassland} & METRIC & 91 & 136 & 88 & 122 & 147 & 1.079 & 0.491 \\  & & TSEB-PT & 108 & 128 & 55 & 79 & 92 & 0.718 & 0.786 \\  & & disTSEB & 106 & 127 & 64 & 82 & 96 & 0.756 & 0.794 \\  & & ESVEP & 79 & 134 & 31 & 93 & 110 & 0.825 & 0.446 \\ \cline{2-10}  & \multirow{4}{*}{savanna} & METRIC & 140 & 165 & \(-\)38 & 78 & 99 & 0.602 & 0.610 \\  & & TSEB-PT & 151 & 160 & 8 & 71 & 88 & 0.549 & 0.642 \\  & & disTSEB & 150 & 161 & 5 & 63 & 79 & 0.490 & 0.723 \\  & & ESVEP & 89 & 215 & \(-\)67 & 117 & 151 & 0.702 & 0.131 \\ \hline \multirow{8}{*}{forest} & METRIC & 7 & 337 & \(-\)64 & 121 & 173 & 0.512 & 0.763 \\  & & TSEB-PT & 17 & 282 & 55 & 109 & 138 & 0.488 & 0.909 \\ \cline{1-1}  & & disTSEB & 17 & 282 & 51 & 118 & 143 & 0.509 & 0.899 \\ \cline{1-1}  & & ESVEP & 13 & 256 & \(-\)93 & 177 & 198 & 0.774 & 0.770 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error dependence on land cover for METRIC, TSEB-PT, disTSEB (TSEB-PT with flux disaggregation) and ESVEP 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 (-). The agriculture class was further split into herbaceous and woody types, with results shown in Table 6. The former sub-class represents crops such as corn, soybean or wheat while the latter represents olive groves and vineyards. TSEB models produce the most consistent results for both types of crops, although somewhat surprisingly the RMSE of \(\lambda E\) in woody crops (76-79 W m\({}^{-2}\)) is significantly lower than in herbaceous crops (91-93 W m\({}^{-2}\)), while opposite is the case for RSME of H (69-71 W m\({}^{-2}\) in herbaceous crops and 91-94 W m\({}^{-2}\)). rRMSE of \(\lambda E\) in both agricultural sub-classes was 0.32 which is of the same magnitude as energy closure gap at the validation sites (e.g., the mean value at CH was 0.34 at the times at which fluxes were modelled).