# Evaluation of Remote Sensing-Based Evapotranspiration
## 4 Results and Discussion
### Calibration with Only Runoff (Scheme I)  
Scheme I is applied in this section, which only uses the runoff observations for parameter calibration. The KGE and PBIAS of runoff are utilized as objective functions. Besides KGE and PBIAS, the RMSE, R, and ME are used to assess the performance of runoff simulation, which are summarized in Table 2. As shown in Table 2, the KGE values in the calibration and validation periods are 0.87 and 0.89, respectively. The PBIAS values are 3.45% and 4.28% in the calibration and validation periods, respectively. Furthermore, all other evaluation indices are within satisfactory ranges [47]. This illustrates that the model parameters are well calibrated. The good performance of runoff simulation based on Scheme I also can be observed in other studies [7,8].  
Figure 5 displays the comparison of simulated and observed daily runoff for 2004 and 2009, which is used to represent the calibration and validation periods, respectively. The reason for selecting 2004 and 2009 as the representative years is that the hydrological characteristics of these two years are similar to the calibration and validation periods, respectively. Furthermore, using only one representative year in the figures is beneficial to compare the runoff observations and simulations clearly. As shown in Figure 5, the simulated runoff is very close to the observed runoff in the calibration period, of which almost all peak flows can be captured well. Meanwhile, in the validation period, the value of PBIAS is higher than that in the calibration period, which is mainly due to the underestimation of peak flows. For the peak flows that occurred in June 2009, the model generates a simulated peak flow with 468 m\({}^{3}\)/s, which is far less than the observation, with 823 m\({}^{3}\)/s. Furthermore, the observed peak flow is 965 m\({}^{3}\)/s in August 2009, while the model-simulated peak flow is only 643 m\({}^{3}\)/s.  
Table 3 presents the evaluation indices of ET simulation with the three remote sensing-based ET datasets. From this table, it is obvious that the ET simulation is unsatisfactory. Among these datasets, the simulated ET can replicate the PML-based ET more accurately than the FLUXCOM-based and GLEAM-based ET in the calibration period, with higher KGE and R, and lower PBIAS, RMSE, and ME. However, the performance of ET simulations in the validation period is similar for all three datasets. Overall, it can be concluded that the model based on Scheme I has a better capability to replicate the PML-based ET compared to the FLUXCOM-based and GLEAM-based ET.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Periods** & **KGE** & **PBIAS (\%)** & **RMSE** & **R** & **ME** \\ \hline Calibration (2004–2008) & 0.87 & 3.45 & 68.51 & 0.89 & \(-\)3.69 \\ Validation (2009–2010) & 0.89 & 4.28 & 99.03 & 0.92 & \(-\)7.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation indices of runoff simulation based on Scheme I.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Periods** & **ET Datasets** & **KGE** & **PBIAS (\%)** & **RMSE** & **R** & **ME** \\ \hline Calibration & PML-V2 & 0.68 & 0.48 & 0.79 & 0.72 & \(-\)0.01 \\ (2004–2008) & FLUXCOM & 0.59 & 22.55 & 1.17 & 0.66 & \(-\)0.55 \\  & GLEAM & 0.58 & 20.18 & 1.22 & 0.63 & \(-\)0.48 \\ \hline Validation (2009–2010) & PML-V2 & 0.61 & 14.93 & 0.81 & 0.73 & 0.27 \\  & FLUXCOM & 0.65 & 14.03 & 1.07 & 0.68 & \(-\)0.34 \\  & GLEAM & 0.62 & 11.92 & 1.12 & 0.66 & \(-\)0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation indices of ET simulation based on Scheme I.