## 4 Results
### Results of Accuracy Assessment and Variable Importance  
In order to quantitatively justify the performance of the proposed method, accuracy assessment was carried out based on the confusion matrix derived from validation data (Table 5). It indicates that the proposed method can extract the inundated areas accurately with an overall accuracy of 94% and a Kappa index of 0.88. Besides, more flooded testing points were misclassified as non-flooded (17) than the situation in which non-flooded were misclassified as flooded (7), meaning that the proposed method has the tendency to underestimate the flooded areas.  
It is necessary to show the accuracy assessment of the overall five land cover classes. To calculate the confusion matrix, 200 testing points were selected for each class and the following Table 6 shows the accuracy assessment results for the entire five land cover types.  
Table 6 indicates that the classification errors mainly occurred between woodland and cropland, water and poilil up area. The spectral similarity between forests and crops accounts for the classification errors between woodland and cropland. Meanwhile, the errors between water (mainly flooded areas), cropland, and built up area lie in the fact that a large amount of croplands and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Class**} & \multicolumn{5}{c}{**Validation Data**} \\ \hline  & **Woodland** & **Cropland** & **Built up** & **Bare soil** & **Water** & **UA (\%)** \\ \hline Woodland & 158 & 0 & 0 & 0 & 0 & 100.00 \\ Cropland & 31 & 173 & 0 & 0 & 0 & 84.80 \\ Built up & 1 & 8 & 200 & 9 & 16 & 85.47 \\ Bare soil & 1 & 0 & 0 & 191 & 0 & 99.48 \\ Water & 9 & 19 & 0 & 0 & 184 & 86.79 \\ PA (\%) & 79.00 & 86.50 & 100.00 & 95.50 & 92.00 & \\ OA (\%) & 90.60 & & Kappa & 0.8825 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for five land cover types.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Classification Results**} & \multicolumn{2}{c}{**Validation Data**} \\ \hline  & **Flooded** & **Non-flooded** & **UA (\%)** \\ \hline Flooded & 183 & 7 & 96.3 \\ Non-flooded & 17 & 193 & 91.9 \\ PA (\%) & 91.5 & 96.5\% & \\ OA (\%) & 94 & Kappa & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for two land cover types.  
built up areas were submerged by the flood water, which resulted in the spectral confusion that accounts for the classification errors.  
Another important characteristic of Random Forest classifier is that it can provide the importance of input variables, which can be used to measure their contribution to the classification accuracy. Actually, the variable importance can play a role in feature selection in choosing the more important variables for further classification [32]. The feature selection process is significant especially when several dozens of variables are used and the amount of calculations are correspondingly heavy [32]. Therefore, it is possible to re-run the RF classifier without the less important variables to reduce the amount of calculations. However, we did not discard any input variables because the number of variables was small in this study and the amount of calculation was acceptable. The importance of all the 13 input variables is depicted in Figure 9. In general, the original reflectance bands are more important than fraction bands and topographical bands. The near-infrared band (Band-4) is the most important followed by the short wave infrared band (Band-6). This is partly because the reflectance of water is very low in those two bands, which helps to increase the difference between flooded and non-flooded regions. As for the fraction bands, the water fraction is more important and this is predictable since water fraction can help provide sub-pixel information of the flooded areas.