## 3 Results and Discussion  
### Level of Contribution of the Time Series Features  
Based on the knowledge about the characteristics and patterns of the time series data for the two polarizations and their combinations, time series features were derived [8]. Using training data for each of the study areas, those features were examined for their relevance to the extraction of the desired classes, TOW and TFV. For this purpose, similarly large training data sets were derived from optical images for the three classes TOW, TFV, and DL and for each of the study areas. Using Random Forest Algorithm, it was identified which time series features provide the highest contribution to the classification results and are, therefore, most relevant for the derivation of flood-related classes. Furthermore, it was examined whether a single time series feature or a combination of time series features enables the highest classification accuracy for the two desired classes. As a result, redundant information can be sorted out and the information required for classification identified. Tables 4 and 5 show the level of contribution of the analyzed time series features for the derivation of the above-mentioned classes and the three study areas. Z-Score VV + VH represents the time series feature with the highest contribution for TOW for all study areas (Table 5). This can be explained by the fact that for both VV and VH polarizations the backscatter values decrease at the analyzed date of the flood [8]. An example of the backscatter value decrease in VV and VH is shown in Figure 6, which was created for a TOW segment from the validation data. Z-Score VV + VH combines both sources of information, whereby the decrease of the backscatter values is intensified. Figure 2b shows the enlarged view of the high-resolution WorldView2 image, demonstrating the presence of the corresponding TOW at the analyzed flood date. Further examples of TOW and the analyzed extent of study areas in southern Greece/Turkey and China are demonstrated in Figures 3a and 5c. In addition to both polarizations, the multi-temporal behavior of the Normalized Difference Vegetation Index (NDVI) values for the same time-period is displayed in Figure 6 serving as a comparison to the SAR time series data. NDVI values were derived from the LANSAT 8 data sets. Despite the cloud-related data gap for 2014, the beginning of 2015 and the beginning of 2016, a strong decrease in the NDVI values for the analyzed date of the flood event can be observed. In combination with the decrease of the backscatter values, this confirms the occurrence of water at the analyzed date. For the classification of TFV, different time series features with the highest contribution or relevance can be identified for the individual study areas. The differences can be attributed to the different types of vegetation and the different environmental conditions in the study areas. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Southern** & **Northern** & **China (\%)** \\  & **Greece/Turkey (\%)** & **Greece/Turkey (\%)** & **China (\%)** \\ \hline Z-Score VV & 35.64 & 29.26 & 31.35 \\ Z-Score VH & 15.51 & 23.07 & 24.61 \\ Z-Score VV + VH & 39.44 & 35.3 & 33.37 \\ Z-Score VV – VH & 5.91 & 7.33 & 3.87 \\ Z-Score VV/VH & 3.51 & 5.08 & 6.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Random forest importance analysis of the time series features for TOW and individual study areas. The rows highlighted in blue represent the time series features with the highest contribution (importance). For the study area in southern Greece/Turkey, the Z-Score VV - VH is the most relevant time series feature for the derivation of TFV (Table 5). This can be explained by the strong difference between the polarizations VV and VH (Figure 7). Thereby, VV polarization shows a strong increase at the flood date compared to the rest of the time series and VH polarization shows low or almost no increase at the flood date.