## 4 Result and Discussion
### Spatial and Temporal Characteristics of the Accuracy Statistics  
For a more detailed examination of the daily ET\({}_{0}\) retrieval, the accuracy statistics were summarized according to location, year, and month. Table 4 shows the accuracy associated with the nine stations in the croplands of South Korea used for in situ measurements. Station 177 was associated with the highest accuracy, with the MAE of 0.703 mm/day, the RMSE of 0.955 mm/day, and the CC of 0.896, whereas station 254 results were the least accurate, with the MAE of 0.878 mm/day, the RMSE of 1.144 mm/day, and the CC of 0.833. The overall accuracy of the daily ET\({}_{0}\) was similar among the nine stations because the ET\({}_{0}\) is not affected by the local condition of the land surfaces. Figure 8 shows scatterplots for the nine stations, in which data trends are generally in accordance with the 1:1 line. The temporal characteristics of the accuracy statistics according to year were also examined. The MAE for each year ranged between 0.745 and 0.834 mm/day; RMSE ranged from 0.996 to 1.092 mm/day; the CC ranged between 0.852 and 0.893. No significant differences among years were found, indicating that the accuracy statistics were stable across years (Table 5; Figure 9) because the input features from the satellite images and numerical reanalysis product well reflect the meteorological and land surface conditions according to the time-series of the seven years. \begin{table}
\begin{tabular}{c c c c c} \hline
**Station No.** & **MBE** & **MAE** & **RMSE** & **CC 1** \\ \hline
129 & 0.381 & 0.795 & 1.016 & 0.857 \\
177 & \(-\)0.037 & 0.703 & 0.955 & 0.896 \\
251 & 0.158 & 0.734 & 0.951 & 0.900 \\
252 & \(-\)0.150 & 0.795 & 1.053 & 0.862 \\
254 & \(-\)0.072 & 0.878 & 1.144 & 0.833 \\
258 & 0.018 & 0.765 & 1.001 & 0.870 \\
263 & 0.015 & 0.838 & 1.098 & 0.850 \\
264 & \(-\)0.234 & 0.809 & 1.058 & 0.884 \\
283 & \(-\)0.124 & 0.761 & 1.029 & 0.898 \\ \hline \end{tabular} \({}^{1}\) p-value \(<\) 0.001.  
\end{table} 
Table 4: Accuracy statistics of the nine stations in the croplands in South Korea, 2013–2019. Figure 7: Relationships between wind speed and the estimation error of ET\({}_{0}\). No correlation was found between them. We investigated the seasonal characteristics of the accuracy statistics according to the month between March and November. The MAE and the RMSE were somewhat lower in colder months (September to March) than in warmer months (April to August). Because soil evaporation and vegetation transpiration levels are low in the cold season, the amount of ET is relatively small (Table 6), and accordingly, the errors are also small (Table 7). However, this result does not mean that the accuracy was better in the cold season than in the warm season. We also calculated the normalized root mean square error (NRMSE), which is the RMSE divided by the mean of the observation values to \begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **MBE** & **MAE** & **RMSE** & **CC 1** \\ \hline
2013 & 0.143 & 0.789 & 1.021 & 0.870 \\
2014 & \(-\)0.092 & 0.790 & 1.015 & 0.868 \\
2015 & \(-\)0.017 & 0.834 & 1.092 & 0.893 \\
2016 & 0.025 & 0.784 & 1.050 & 0.852 \\
2017 & \(-\)0.034 & 0.813 & 1.071 & 0.856 \\
2018 & \(-\)0.004 & 0.778 & 0.996 & 0.875 \\
2019 & 0.030 & 0.745 & 1.015 & 0.873 \\ \hline \end{tabular} \({}^{1}\)_p_-value \(<\) 0.001.  
\end{table} 
Table 5: Accuracy statistics of the ML-based ET\({}_{0}\) model in the croplands in South Korea, according to year. Figure 8: Scatterplots for the observed vs. estimated daily ET\({}_{0}\) in the blind test of the ML model for the nine stations in the croplands of South Korea, 2013–2019. account for seasonal variations in the value ranges. Table 7 shows the accuracy statistics by month, including the NRMSE. In contrast to the RMSE, the NRMSE values were similar across all months, indicating that the ET\({}_{0}\) retrievals were stable irrespective of the month (Figure 10). The accuracy was lowest in August, presumably because the quality of satellite data is lowered in the cloudier season.