## 3 Results
Among the PS predictor variables, the NIR spectral band was the most important variable (the sixth most important variable in the overall ranking). The variables that least contributed to RF-based imputation model prediction performance were the B spectral bands of both PS and S2 images. The plot of true colour composites (RGB:321) of the original UAV orthomosaic and imputed UAV orthomosaic for the three levels of missing parts for a visual inspection of the prediction performance of the proposed framework is shown in Figure 6. We also selected three regions (squares #1, #2, and #3 in Figure 6) within the image to show in more detail the UAV orthomosaic imputed parts in the highest level of missing parts assessed in this study (MP-50%). We observed that the RF-based imputation models could reconstruct abrupt changes in the image, such as sparse trees (squares #1 and #2), tree rows (square #2), paddock divisions (square #3), and terrace edges present in the grass-dominated vegetation (squares #1, #2, and #3). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Missing Data** \\ **Percentage** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Predictor** \\ **Imagery** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Agreement** \\ **Parameter** \\ \end{tabular} } & \multicolumn{6}{c}{**Spectral Variable**} \\ \cline{3-10}  & & & & **Blue** & **Green** & **Red** & **Red-Edge** & **NIR** & **NDVI** \\ \hline \multirow{4}{*}{\(10\%\)} & \multirow{2}{*}{PS} & r & 0.50 & 0.59 & 0.52 & 0.59 & 0.58 & 0.54 \\  & & d & 0.66 & 0.74 & 0.68 & 0.73 & 0.72 & 0.70 \\ \cline{2-10}  & \multirow{2}{*}{S2} & r & 0.65 & 0.67 & 0.59 & 0.61 & 0.55 & 0.55 \\  & & d & 0.77 & 0.79 & 0.72 & 0.54 & 0.62 & 0.70 \\ \cline{2-10}  & \multirow{2}{*}{PS + S2} & r & 0.70 & 0.73 & 0.67 & 0.72 & 0.66 & 0.62 \\  & & d & 0.80 & 0.82 & 0.77 & 0.82 & 0.77 & 0.74 \\ \hline \multirow{4}{*}{\(30\%\)} & \multirow{2}{*}{PS} & r & 0.49 & 0.57 & 0.54 & 0.53 & 0.51 & 0.56 \\  & & d & 0.63 & 0.71 & 0.67 & 0.68 & 0.68 & 0.70 \\ \cline{2-10}  & \multirow{2}{*}{S2} & r & 0.69 & 0.66 & 0.65 & 0.53 & 0.53 & 0.60 \\  & & d & 0.80 & 0.79 & 0.77 & 0.68 & 0.70 & 0.73 \\ \cline{2-10}  & \multirow{2}{*}{PS + S2} & r & 0.73 & 0.72 & 0.70 & 0.68 & 0.61 & 0.66 \\  & & d & 0.82 & 0.82 & 0.80 & 0.79 & 0.74 & 0.77 \\ \hline \multirow{4}{*}{\(50\%\)} & \multirow{2}{*}{PS} & r & 0.45 & 0.58 & 0.51 & 0.53 & 0.50 & 0.54 \\  & & d & 0.62 & 0.72 & 0.67 & 0.67 & 0.67 & 0.69 \\ \cline{2-10}  & \multirow{2}{*}{S2} & r & 0.64 & 0.65 & 0.57 & 0.62 & 0.53 & 0.57 \\  & & d & 0.77 & 0.78 & 0.72 & 0.76 & 0.69 & 0.72 \\ \cline{2-10}  & & r & 0.69 & 0.71 & 0.66 & 0.67 & 0.60 & 0.63 \\ \cline{2-10}  & \multirow{2}{*}{PS + S2} & d & 0.79 & 0.81 & 0.77 & 0.78 & 0.73 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spatial agreement between original and imputed UAV data. The NDVI images calculated using the imputed R and NIR spectral bands and the NDVI images predicted directly by the RF-based imputation models showed the same spatial agreement with the original UAV NDVI images in the three levels of missing parts (Table 3). Figure 7 also shows the high spatial agreement between the original UAV NDVI image and the predicted and calculated NDVI image, highlighting the potential of the imputed spectral bands to further derive other vegetation indices. Figure 5: The relative importance of the predictor variables was measured by the variable importance metric in the eighteen RF-based imputation models using both PlanetScope and Sentinel-2 data. Figure 6: Comparison between the true colour composite (RGB:321) of the original UAV orthomosaic (**a**) and the imputed UAV orthomosaic—10% (**b**), 30% (**c**), and 50% (**d**) of image prediction. Squares #1, #2, and #3 show, in more detail, the original UAV orthomosaic, the introduced missing parts, and the imputed UAV orthomosaic in the highest level of missing parts assessed in this study (MP–50%).