## 5 Discussion  
Concerning virtual image generation, this study utilized all of the available spectral bands as inputs in the representation stage for Sentinel-2-based feature extraction. From the experiment results, the use of VNIR bands could improve the prediction accuracy for the prediction of the red-edge and SWIR bands, and vice versa. The feature extraction from highly correlated additional spectral bands could improve prediction accuracy. In this study, after resampling the red-edge and SWIR bands with a spatial resolution of 20 m to 10 m, the resampled bands were fed into the representation stage, which extracted features, including all the spectral information, via a convolution operation. When multi-spectral bands have different spatial resolutions (i.e., Sentinel-2), a simple resampling method, such as nearest neighbor and bilinear, may not properly account for the differences in the spatial resolution. Although the improved prediction accuracy could be obtained from CGAN\({}_{25}\), the CGAN\({}_{25}\) prediction still included the blurred spectral reflectance of the red-edge and SWIR bands. To generate fine-scale band images that maintain the original spectral information, it is necessary to apply advanced spatial downscaling and super-resolution methods, including area-to-point regression kriging [76] and deep neural networks [77; 78]. In future work, it will be worthwhile to analyze the effects of spatial resolution enhancement on the prediction of reflectance. Previous studies on the virtual optical image generation using SAR imagery and CGANs reported that loss of texture information of surface objects and distortion in reflectance were still observed in the generated optical imagery [40; 79]. A significant increase in prediction performance was achieved in the CGAN\({}_{25}\) prediction in this study. However, relatively large errors at parcel boundaries and distortion in reflectance were also observed in the CGAN\({}_{25}\) prediction, as shown in Figure 12. Higher prediction errors did not directly result in misclassification in this study. Since virtual spectral bands are directly used as inputs for crop classification, the generation of virtual imagery with fewer errors is a prerequisite for reliable early crop mapping. In this study, the comparison between CGAN\({}_{25}\) and other CGAN-based models was not performed because the main focus was on the evaluation of prediction performance with respect to the different input feature combinations, which was adopted in previous CGAN-based and application-oriented studies [42; 53]. Despite the promising early crop mapping results, it is still challenging to create reliable virtual optical imagery due to the difference in imaging mechanism between SAR and optical images. From a methodological point of view, reliable virtual optical images can be generated through the use of previous time pairs of SAR and optical images as additional information [40], the modification of model structures, and loss functions [79; 80; 81]. Thus, further methodological developments and comparisons with other advanced CGAN-based models should be performed to improve the prediction performance of virtual image generation. In this study, the subarea in Illinois state was selected as the experimental pilot study area. Early crop mapping results are usually used as inputs for crop yield forecasting and crop acreage estimation on a regional scale (e.g., state unit) [2; 3; 5]. Several aspects of virtual image generation should be further considered when extending the study area. When the virtual optical imagery should be generated over a large region, several images must be stitched to generate the large-scale optical imagery. Stitching several virtual images acquired along different paths inevitably includes uneven seam lines of reflectance between the scene boundaries. Hence, the generation of large-scale seamless virtual optical imagery requires seam elimination, such as weighted averaging [47].