## 3 Results
### Accuracy of Three FVC Estimation Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathrm{FVC}\_1\) & \(\mathrm{FVC}\_2\) & \(\mathrm{FVC}\_3\) \\ \hline RMSE & 0.128 & 0.070 & 0.124 \\ BIAS & 0.096 & \(-\)0.003 & \(-\)0.104 \\ \hline \hline  & & \(\mathrm{FVC}\_1\) & \\ \cline{2-4}  & \(\mathrm{R_{wall}=R_{\mathrm{roof}}\) (10:00 am) & \(\mathrm{R_{wall}=R_{\mathrm{roof}}\) (12:00 pm)} & \(\mathrm{R_{wall}\
eq R_{\mathrm{roof}}\) (10:00 am)} & \(\mathrm{R_{wall}\
eq R_{\mathrm{roof}}\) (12:00 pm)} \\ \cline{2-4} RMSE & 0.163 & 0.084 & 0.162 & 0.074 \\ BIAS & 0.133 & 0.057 & 0.148 & 0.048 \\ \hline \hline  & \(\mathrm{FVC}\_2\) & \\ \cline{2-4}  & \(\mathrm{R_{wall}=R_{\mathrm{roof}}\) (10:00 am) & \(\mathrm{R_{wall}=R_{\mathrm{roof}}\) (12:00 pm)} & \(\mathrm{R_{wall}\
eq R_{\mathrm{roof}}\) (10:00 am)} & \(\mathrm{R_{wall}\
eq R_{\mathrm{roof}}\) (12:00 pm)} \\ \cline{2-4} RMSE & 0.074 & 0.073 & 0.060 & 0.077 \\ BIAS & 0.010 & \(-\)0.049 & 0.018 & \(-\)0.057 \\ \hline \hline  & \(\mathrm{FVC}\_3\) & \\ \cline{2-4}  & \(\mathrm{R_{wall}=R_{\mathrm{roof}}\) (10:00 am) & \(\mathrm{R_{wall}=R_{\mathrm{roof}}\) (12:00 pm)} & \(\mathrm{R_{wall}\
eq R_{\mathrm{roof}}\) (10:00 am)} & \(\mathrm{R_{wall}\
eq R_{\mathrm{roof}}\) (12:00 pm)} \\ \cline{2-4} RMSE & 0.107 & 0.141 & 0.095 & 0.146 \\ BIAS & \(-\)0.079 & \(-\)0.129 & \(-\)0.074 & \(-\)0.135 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of different FVC estimation algorithms. Figure 7: Range of absolute values (\(\rm|\,diff_{\rm VIC}\,|\)) of the FVC difference between the predefined \(\rm FVC_{\rm true}\) and the FVC estimates by three types of algorithms in urban scenes S1 to S25 (Figure 1): (**a**) FVC = 0.1; (**b**) FVC = 0.2; and (**c**) FVC = 0.3. It can be observed that when FVC = 0.1, the estimation accuracies by FVC_1 and FVC_2 are significantly and positively correlated with the building density. In contrast, the estimation accuracies by FVC_3 display a negative correlation with the building density (Figure 7a). When FVC = 0.2, the estimation accuracy by FVC_1 remains positively correlated with the building density, and the estimation accuracy by FVC_3 continues to exhibit a negative correlation. However, the relationship between the estimation accuracy of FVC_2 and the building density is not yet clear when H/W = 0.75. As H/W increases, the FVC estimation accuracy initially exhibits a positive correlation with the building density, followed by a negative correlation, with the turning point in this correlation shifting toward a lower building density. This suggests that the estimation accuracy of FVC_2 for medium vegetation cover is significantly reduced when both the building height and density are high (Figure 7b). When FVC = 0.3, the estimation accuracy of both FVC_2 and FVC_3 is negatively correlated with the building density, and the relationship between the estimation accuracy of FVC_1 and the building density is analogous to the pattern observed in FVC_2 when FVC = 0.2 (Figure 7c). The longer boxes indicate that the FVC estimation accuracy is significantly influenced by the sunlight conditions and building materials, suggesting that the algorithm is unstable (Figure 7). As the FVC increases, the stability of FVC_1 is shown to be \"high FVC > medium FVC > low FVC\". In contrast, the stability of FVC_2 is relatively constant, while the stability of FVC_3 is shown to be \"low FVC > medium FVC > high FVC\". Additionally, as for the FVC estimation accuracy (Figure 7), FVC_1 exhibited a trend of \"high FVC > medium FVC > low FVC,\" FVC_2 demonstrated a pattern of \"medium FVC > low FVC > high FVC\", and FVC_3 displayed a pattern of \"low FVC > medium FVC > high FVC\". It is therefore proposed that the estimation accuracy of FVC_3 is optimal and stable when the vegetation cover is low. In the case of medium vegetation cover, the estimation accuracy of FVC_2 is optimal and stable.