## 2 Materials and Methods
### Training Dataset Preparation  
In this study, we selected the green and NIR spectral bands from L8, along with the green, narrow NIR, and red edge bands (RE1, RE2, RE3) from S2. Table 2 shows the details of the wavelength ranges and spatial resolutions of these selected bands. As described in the methodology in Section 2 (Figure 2), we generated five separate training datasets for the translation of each S2 band from its corresponding L8 band. While the trend in the literature involves employing a deep learning model to learn multiple bands in one single task, we argue that this approach increases the task's complexity. In contrast, training separate models for each spectral band allows each model to focus on learning the representations solely between two specific bands. Each L8 band was resampled to match the spatial resolution of its corresponding S2 band using nearest neighbor interpolation; for instance, L8 green at 30 m was resampled to S2 green at 10 m. This technique was chosen to ensure that the original surface reflectance values remained unchanged, as opposed to using bilinear or bicubic. Given that the L8 green band also serves to predict S2 RE1, we matched their spatial resolutions during the data pre-processing steps by resampling 20 m S2 RE1 to 10 m. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pairs** & **L8 Path/Row** & **Date** & **S2 Tile IDs** & **Date (2019)** \\ \hline
1 & 034/030 & 4 October & 13 TH, 13 TEH, 13 TEJ & 4 October \\
2 & 031/034 & 15 October & 135 GB, 13 SGC & 13 October \\
3 & 027/037 & 19 October & 14 SPB & 22 October \\
4 & 028/039 & 26 October & 14 RMU, 14 RMV, 14 RNU, 14 RNV & 27 October \\
5 & 034/034 & 5 November & 13 SCB & 8 November \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the L8 and S2 scene pairs used in this study, including the path/row of L8 and the tile ID of the S2 scenes, their acquisition dates, and the time difference in days between each overlapping scene within a pair. Figure 1: Training pair examples—(**A1**) L8 NIR and (**A2**) S2 Red Edge 2 (cloud free); (**B1**) L8 NIR (cloud obscured with shadow); (**B2**) S2 Red Edge 3. The L8 and S2 imagery were then spatially aligned through clipping them. However, we encountered spatial registration inconsistencies between L8 and S2, a phenomenon also observed by [PERSON] et al. [42] and attributable to spatial resolution disparities. For example, a 30 m L8 pixel may encapsulate up to nine 10 m S2 pixels, leading to unavoidable misregistration issues as emphasized by [PERSON] et al. [43]. To reduce this, we employed a phase correlation technique [44] to reduce this error. Keeping S2 constant, we slid the L8 image in each direction to find the coordinates with the highest correlation. After reducing misregistration errors and spatially aligning each pair, we extracted image patches of dimensions 256 \(\times\) 256 from both the L8 and S2 datasets. The choice of a 256 \(\times\) 256 patch size was a trade-off between capturing sufficient spatial context for the model and the computational constraints imposed by our GPU capabilities. Opting for larger image patches would necessitate reducing the batch size, a modification that could increase the risk of model overfitting. Due to the resampling of L8 green and S2 RE1 to a 10 m spatial resolution (refer to Section 2, Figure 2), the dataset comprised 7552 image pairs for these particular bands. Similarly, the dataset for the 20 m S2 NIR, S2 RE2, S2 RE3, and the resampled L8 NIR (from 30 m to 20 m) contained 3291 training images each. For the purposes of model validation and testing, we randomly partitioned 5% of the total data into separate validation and test sets. These subsets consisted of 375 images for the 10 m dataset and 80 image patches for the 20 m dataset.