## 4 Discussion  
This study proposed a framework to impute missing parts of UAV orthomosaics based on data intercalibration and the well-known RF algorithm using PS and S2 data. The positive results of the RF-based imputation models with RMSE below 20% (Table 1) and the high spatial agreement (d \(>\) 0.74) between the imputed and the original pixel values in the simulated artificial missing parts of the UAV orthomosaic (Table 2) demonstrated the proposed framework potential to impute missing parts for both spectral bands and vegetation indices derived from UAV orthomosaics. No spatial imputation method perfectly restores a remotely sensed image [10; 41]. However, slight visual differences occurred between imputed and non-imputed pixels in the UAV orthomosaics (Figure 6). Furthermore, there was a very similar spatial agreement between the original NDVI with the NDVI calculated from the predicted spectral bands and the NDVI predicted directly by the RF-based imputation models. It indicates that the prediction error of the spectral bands (R and NIR) was equivalent to the error of predicting the NDVI directly (Table 3 and Figure 7). These results have a practical implication for indicating that several vegetation indices of agronomic interest can be calculated from the imputed UAV spectral bands, illustrating the practical importance of the proposed framework. Much of the success of the proposed framework can be attributed to the RF algorithm's high learning capacity [34; 37] and the extensive data samples provided for model training and validation. Those conditions allowed the RF-based imputation models to capture the complex relationships between the spectral bands and vegetation indices derived from the same or different sensor images. The RF algorithm also successfully filled gaps in satellite image time-series data using spatial-temporal statistics in the study of [23]. The success of restoring up to 50% of missing parts in the UAV orthomosaic in our study indicates the flexibility of the proposed framework to deal with different levels of missing parts. Moreover, by using data derived from images obtained from the same area and at the same time (even with different spatial and spectral resolutions) as predictor variables, the proposed framework could predict discontinuities and abrupt changes in the landscape, such as sparse trees, tree rows, paddock divisions, and terrace edges (Figure 6). Restoring discontinuities and abrupt changes in the landscape has been reported in the literature as a challenge for the spatial imputation methods of remotely sensed images [11; 41; 42], corroborating the potential of using the proposed framework to impute missing parts in remotely sensed imagery. The combined spatial and spectral characteristics of PS and S2 images resulted in the superior performance of the RF-based imputation models using both data instead of using just one of them, likely due to the association between the high spatial resolution of PS images and the high spectral resolution of S2 images (Tables 1 and 2). The contribution of each satellite was better understood when we analysed the models' performance using predictor variables derived from PS and S2 separately. The RF-imputation models built using S2 data outperformed those built using PS data. In our study, the S2 images' spectral characteristics were more relevant to imputing UAV orthomosaic missing parts than the PS images' high spatial resolution. The S2 has a relatively high number of spectral bands with narrow widths in which the central wavelengths are close to the central wavelengths of UAV spectral bands (Figure 2). By analysing the overall relative importance of the predictor variables (Figure 5), the S2 spectral contribution in the RF-based imputation models became even more evident, since the five most important variables in the imputation of the missing parts in the UAV orthomosaic were five S2 spectral bands (RE4, SWIR1, RE1, G, and SWIR2).