## 4 Results  
### Virtual Image Generation Results  
#### 4.1.1 Prediction Performance for SAR-Based Feature Combinations  
When comparing the prediction performance of the different SAR-based features of CGAN25, the inclusion of the RVI to the dual-polarization backscattering coefficients exhibited the best prediction accuracy for all of the spectral bands (Table 8). The maximum improvement in the RMSE of the case using all of the SAR features over the case without RVI was 8.97% for the green band. When comparing the relative improvement in the different spectral bands, the relative improvement in RMSE was substantial for the VNIR bands compared to the red-edge and SWIR bands. The structural similarity and PSNR were also improved by adding the RVI as additional feature information. The RVI improved the SSIM by 3.2%p and 2.4%p for the NIR and red-edge3 bands, respectively.  
Figure 10 shows the prediction results with the actual Sentinel-2 imagery zoomed in on the subarea for a visual comparison. The local details in the actual imagery were not perfectly depicted in the two prediction results. However, the prediction using RVI  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Input Images** \\ \hline
1 & 10 June \\
2 & 10 June + 25 June \\
3 & 10 June + 25 June + 13 July \\
4 & 10 June + 25 June + 13 July + 30 July (actual Sentinel-2 image) \\
5 & 10 June + 25 June + 13 July + 30 July (virtual Sentinel-2 image) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Incremental classification cases considered for early crop mapping.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Band**} & \multicolumn{5}{c}{**CGAN25 without RVI**} & \multicolumn{5}{c}{**CGAN25 with RVI**} \\ \cline{2-9}  & **RMSE** & **CC** & **SSIM** & **PSNR** & **RMSE** & **CC** & **SSIM** & **PSNR** \\ \hline Blue & 0.021 & 0.557 & 0.934 & 33.410 & 0.020 & 0.614 & 0.945 & 34.192 \\ Green & 0.022 & 0.615 & 0.932 & 33.021 & 0.020 & 0.673 & 0.944 & 33.848 \\ Red & 0.028 & 0.661 & 0.894 & 31.190 & 0.025 & 0.708 & 0.913 & 31.987 \\ NIR & 0.070 & 0.766 & 0.688 & 23.152 & 0.064 & 0.799 & 0.720 & 23.846 \\ RE1 & 0.024 & 0.778 & 0.920 & 32.583 & 0.020 & 0.815 & 0.931 & 33.197 \\ RE2 & 0.045 & 0.789 & 0.811 & 26.947 & 0.043 & 0.820 & 0.828 & 27.435 \\ RE3 & 0.061 & 0.798 & 0.732 & 24.332 & 0.057 & 0.833 & 0.756 & 24.816 \\ SWIR2 & 0.029 & 0.844 & 0.906 & 30.862 & 0.027 & 0.864 & 0.921 & 31.376 \\ SWIR3 & 0.024 & 0.811 & 0.921 & 32.350 & 0.023 & 0.832 & 0.932 & 32.856 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy statistics for different SAR feature combinations (RE: red-edge; NIR: near infrared; SWIR: shortwave infrared; RMSE: root mean square error; CC: correlation coefficient; SSIM: structural similarity; PSNR: peak signal-to-noise ratio).  
as additional feature information could reproduce the overall patterns better than the prediction using only backscattering coefficients. In particular, the parcel boundaries were well depicted in the prediction using RVI, as confirmed by the higher SSIM value in Table 8. These comparison results confirm the potential of using the RVI for the extraction of valuable information for vegetation discrimination.