## 3 Results  
Combining predictor variables derived from PS and S2 images resulted in the RF-based imputation models with the lowest predicted errors (Table 1) and the highest spatial agreement between imputed and observed values in the extent of the missing parts (Table 2). The NIR spectral band showed the lowest RMSE values (RMSE < 7%), whereas the NDVI showed the highest RMSE values (RMSE > 17%). Our results indicated that the S2 models outperformed the PS models in predicting the missing parts of the UAV orthomosaic. Additionally, the levels of missing parts in the UAV orthomosaic did not significantly affect the prediction performance of the RF-based imputation models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Missing Part**} & \multirow{2}{*}{**Predictor**} & \multirow{2}{*}{**Error**} & \multicolumn{6}{c}{**Spectral Variable**} \\ \cline{3-8}  & & & **Blue** & **Green** & **Red** & **Red-Edge** & **NIR** & **NDVI** \\ \hline \multirow{8}{*}{10\%} & \multirow{4}{*}{PS} & RMSE & 0.0068 & 0.0121 & 0.0170 & 0.0209 & 0.0142 & 0.0814 \\  & & RMSE\% & 12.33 & 9.59 & 18.18 & 8.84 & 7.97 & 18.81 \\  & & R\({}^{2}\) & 0.36 & 0.48 & 0.39 & 0.43 & 0.46 & 0.43 \\ \cline{2-8}  & \multirow{4}{*}{S2} & RMSE & 0.0059 & 0.0112 & 0.0160 & 0.0211 & 0.0133 & 0.0797 \\  & & RMSE\% & 10.73 & 8.89 & 17.07 & 8.92 & 7.44 & 18.44 \\  & & R\({}^{2}\) & 0.48 & 0.58 & 0.43 & 0.50 & 0.61 & 0.38 \\ \cline{2-8}  & \multirow{4}{*}{PS + S2} & RMSE & 0.0055 & 0.0103 & 0.0148 & 0.0192 & 0.0122 & 0.0745 \\  & & RMSE\% & 9.99 & 8.17 & 15.77 & 8.12 & 6.82 & 17.23 \\  & & R\({}^{2}\) & 0.50 & 0.52 & 0.43 & 0.46 & 0.53 & 0.40 \\ \hline \multirow{8}{*}{30\%} & \multirow{4}{*}{PS} & RMSE & 0.0068 & 0.0121 & 0.0170 & 0.0209 & 0.0142 & 0.0814 \\  & & RMSE\% & 12.35 & 9.66 & 18.19 & 8.85 & 7.96 & 18.82 \\  & & R\({}^{2}\) & 0.35 & 0.48 & 0.37 & 0.46 & 0.46 & 0.43 \\ \cline{2-8}  & \multirow{4}{*}{S2} & RMSE & 0.0059 & 0.0112 & 0.0160 & 0.0211 & 0.0133 & 0.0799 \\  & & RMSE\% & 10.77 & 8.92 & 17.08 & 8.92 & 7.49 & 18.48 \\  & & R\({}^{2}\) & 0.48 & 0.59 & 0.42 & 0.51 & 0.63 & 0.38 \\ \cline{2-8}  & \multirow{4}{*}{PS + S2} & RMSE & 0.0055 & 0.0103 & 0.0148 & 0.0192 & 0.0121 & 0.0747 \\  & & RMSE\% & 10.03 & 8.19 & 15.75 & 8.12 & 6.82 & 17.28 \\  & & R\({}^{2}\) & 0.45 & 0.52 & 0.41 & 0.47 & 0.47 & 0.40 \\ \hline \multirow{8}{*}{50\%} & \multirow{4}{*}{PS} & RMSE & 0.0069 & 0.0122 & 0.0172 & 0.0211 & 0.0142 & 0.0818 \\  & & RMSE\% & 12.49 & 9.68 & 18.38 & 8.91 & 7.98 & 18.91 \\  & & R\({}^{2}\) & 0.38 & 0.48 & 0.39 & 0.49 & 0.46 & 0.45 \\ \cline{2-8}  & \multirow{4}{*}{S2} & RMSE & 0.0058 & 0.0111 & 0.0158 & 0.0158 & 0.0132 & 0.0798 \\  & & R\({}^{2}\) & 10.64 & 8.83 & 16.91 & 16.91 & 7.40 & 18.46 \\  & & R\({}^{2}\) & 0.50 & 0.58 & 0.45 & 0.45 & 0.62 & 0.40 \\ \cline{2-8}  & \multirow{4}{*}{PS + S2} & RMSE & 0.0055 & 0.0102 & 0.0147 & 0.0191 & 0.0121 & 0.0749 \\  & & RMSE\% & 9.95 & 8.15 & 15.72 & 8.10 & 6.77 & 17.33 \\ \cline{2-8}  & & R\({}^{2}\) & 0.47 & 0.52 & 0.43 & 0.48 & 0.53 & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of the random forest models in imputing the missing parts of the UAV orthomosaic using PlanetScope (PS) and Sentinel-2 (S2) data. The predictions of the five spectral bands of the UAV orthomosaic (B, G, R, RE, and NIR) and the NDVI, retrieved for the best RF-based imputation models using the combination of PS and S2 data, showed a good agreement with the observed values in the testing dataset (not shown here). The lowest values of the spectral variables were overestimated, whereas the highest observed values were underestimated. The RE4, SWIR1, RE1, G, and SWIR2 spectral bands from the S2 image were the five most important variables (Figure 5) in computing the missing parts in the UAV orthomosaic. They were measured according to the overall relative importance of the predictor variables, as measured by the variable importance metric in the eighteen RF-based imputation models using both PS and S2 data.