## 4 Results
### Crop Classification Results  
The virtual spectral bands predicted using CGAN\({}_{\text{2S}}\) were used as inputs for the incremental classification (case 5 in Table 7). The classification accuracy values of the incremental classification results for different Sentinel-2 input spectral bands are shown in Figure 13. As more input Sentinel-2 images were used for classification, the classification accuracy increased regardless of the CGAN model type. When comparing the effects of using different input spectral bands, the poorest OA was achieved for the case using VNIR bands for all cases. Except for cases 1 and 2, the difference in OA between using red-edge and SWIR bands and all of the spectral bands was insignificant for cases 3 to 5.  
Cases 1 to 2, where the Sentinel-2 images acquired before July were used as inputs for classification, achieved poor classification accuracy values of less than 80%. The OA of case 3 using the Sentinel-2 image on 13 July was larger than that of case 2; however, it was still lower than cases 4 and 5. The inclusion of the Sentinel-2 imagery on 30 July for cases 4 and 5 could significantly increase the classification accuracy, achieving an OA value of more than 90%. The best OA was achieved for case 4, which used the actual Sentinel-2 imagery on 30 July. Case 5, which used the virtual Sentinel-2 imagery predicted by CGAN, yielded an OA comparable to case 4. The difference in OA between cases 4 and 5 was approximately 1.72%p on average for the three different inputs. When comparing case 3, the inclusion of all of the spectral bands of the virtual Sentinel-2 imagery on 30 July increased the OA by 5.03%p. Despite the errors in the virtual spectral bands predicted by CGAN\({}_{\text{2S}}\), the promising accuracy for case 5, which is higher than case 3 and comparable to case 4, indicates the feasibility of the virtual optical image generation for early crop mapping.  
Figure 12: Error distributions over the study area of NIR and red-edge3 bands for different CGAN models with the CDL.  
To further analyze the classification result, the classification map of case 5 using all of the spectral bands was compared with the CDL, which was regarded as the ground truth, and a classification error map was generated (Figure 14). As only crop parcels were considered for classification, non-crop areas were masked in the error map. Notably, the parts with large error values in Figure 12 were correctly classified, which implies that the errors in spectral reflectance did not significantly affect the classification performance. From the error map in Figure 14, most misclassification occurred along the parcel boundaries, except for some crop parcels. The resampling of the original 30 m CDL to 10 m may not fully represent the actual land-cover type near the parcel boundary, which resulted in misclassification along the parcel boundaries. The actual crop type in the misclassified crop parcels in Figure 14 (black color) was mostly soybean and was misclassified as alfalfa. However, as alfalfa is a minor crop type in the study area, this misclassification did not significantly affect the overall accuracy. From both the quantitative and the qualitative evaluation results, it can be concluded that the cloud-free virtual imagery generated at the optimal date for early crop mapping could be a practical input for crop classification and generate reliable results.  
Figure 14: Comparison of the classification result of case 5 using all bands with CDL and the classification error map.  
Figure 13: Overall accuracy values of five different incremental classification results for different input combinations. The case numbers can be referred to in Table 7.