## 3 Results
### Translating S2-like RE1, RE2, and RE3 Bands from L8  
Figure 7 shows the S2-like RE1, RE2, and RE3 translations by CGAN using two different approaches--direct and multistep. The objective for both of these approaches was to downscale the 30 m L8 green bands to 20 m S2-like RE1 bands at a scale factor of 1.5. However, to accommodate the multistep approach, all images are resampled to 10 m to match the resolution of the S2 green band. From the results, we observe a noticeable improvement in the spatial resolution from the original L8 from both direct and multistep approaches. We observe that both strategies were effective at translating S2-like RE1 bands (Figure 7(A2)) that are spectrally more similar to the target S2 RE1 image compared to the L8 green image. Since the target S2 RE1 images were resampled to a higher resolution of 10 m compared to its original resolution of 20 m, the CGANs in the direct approach were able to reconstruct the spatial features at a finer spatial resolution. We observe that the direct approach produced visually better results compared to the multistep approach. Interestingly, resampling the image to a higher spatial resolution also allowed the direct CGAN to produce sharper images than the 20 m S2 RE1. In contrast, the multistep approach produced images that are spatially and texturally similar to the original 20 m S2 RE1. This is due to the additional step in which the CGAN first translates the L8 green to S2-like green and then translates the S2-like green image to the target S2-like RE1. The additional step allows the network to learn the spatial representations between the input and the target images more accurately, hence producing images that are qualitatively and quantitatively more similar to the target 20 m S2 RE1 images. This feature of the multistep approach is observed in its better quantitative performance compared to the direct approach (Table 5). Similarly, the two approaches were used to translate 20 m S2-like RE2 and RE3 images from 30 m L8 NIR. From Figure 7 we observe that both approaches improved the spatial resolution of the predicted S2-like RE2 and RE3 images from L8 NIR. Unlike the case for predicting the S2-like RE1 bands, predicting the RE2 and RE3 bands did not require further resampling steps for spatial resolution matching. S2 NIR, which is required as an intermediate step for the multistep method, and the target RE2 and RE3 images have the same spatial resolution at 20 m. Therefore, it is not surprising that the multistep method took advantage of an additional learning step and produced sharper images compared to the direct method (Figure 7(B2,B3,C2,C3)). From the quantitative evaluation results reported in Table 5, we observe that the performance of both direct and multistep approaches is comparable as they minimize the gap between L8 NIR and the S2-like RE2 and RE3 bands. We layer stacked S2-like RE1, RE2, and RE3 bands translated by both the approaches (Figure 8), to compare spatial and spectral reconstruction. Each RGB image is layer-stacked with RE3, RE1, and RE2 in that order, except the L8 scene. The L8 scene in Figure 8A is layer-stacked with the spectral bands with which each RE band was predicted, i.e., NIR (for predicting SR3), green (for predicting SR1), and NIR (for predicting SR2). Overall, we observe that both direct (Figure 8B) and multistep (Figure 8C) approaches reduced the spectral gap between the L8 and the target S2 images significantly. However, discernible differences can be observed in the spectral reconstruction performances of the two approaches. Images produced by the direct approach exhibit artificial noise, while those generated through the multistep method appear visibly cleaner. The additional learning step in the multistep method allows for a more stable translation performance.