## 3 Results and Discussion
### Level of Contribution of the Time Series Features
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Southern** & **Northern** & **China (\%)** \\  & **Greece/Turkey (\%)** & **Greece/Turkey (\%)** & **China (\%)** \\ \hline Z-Score VV & 32.61 & 28.39 & 33.21 \\ Z-Score VH & 2.79 & 11.30 & 17.32 \\ Z-Score VV + VH & 18.66 & 21.53 & 33.84 \\ Z-Score VV - VH & 41.57 & 16.37 & 8.56 \\ Z-Score VV/VH & 4.38 & 22.41 & 7.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Random forest importance analysis of the time series features for TFV and individual study areas. The rows highlighted in green represent the time series features with the highest contribution (importance). Figure 6: Multitemporal behavior of the backscatter intensity for TOW areas for VV (decibel (dB)), VH (dB), VV/VH (linear scale), and Normalized Difference Vegetation Index (NDVI) values in northern Greece/Turkey. The blue bars mark the analyzed date at the flood event. The different behavior of both polarizations can be explained by the different sensitivity of VV and VH to specific backscattering mechanisms, which caused an increased difference in the backscatter values between the two polarizations at the analyzed date of the flood. In comparison, the difference between the polarizations for the dry dates shows a smaller variability. The different sensitivity is on the one hand due to the fact that the double-bounce effect occurs more strongly in the VV polarization and leads to the backscatter. On the other hand, backscatter is in general lower for cross-polarization (HV or VH), as no ideal corner reflectors can be produced due to their depolarizing properties [5; 58; 59]. As a result, it is reported that the increase of the backscatter values by TFV can be more reliably detectable by co-polarized data than by cross-polarized data [60]. However, the combination of co- and cross-polarized data can improve the identification of TFV [26]. The NDVI values in Figure 7 confirm that the increase in VV polarization was not caused by seasonal or other changes in vegetation, but is flood-related, as no sudden increase in NDVI values could be identified at the flood date. For the study area in northern Greece/Turkey, Z-Score VV is the most relevant feature (Table 5). As previously mentioned in Section 2.1, this study area is dominated by deciduous forest (Figure 2a), which was under leaf-on conditions at the date of the flood. It should be noted that the penetration of the forest canopy may be restricted by C-band and environmental parameters (e.g., aboveground biomass) [61; 62]. [PERSON] and [PERSON] [63] reported that the SAR backscatter values increase depending on the biomass and that the soil signal can no longer be detected if a certain degree of saturation has been reached. Thereby, the volume scattering from the canopy completely superimposes the contribution of the double bounce effects from the interaction between the water surface and vertical vegetation structure and TFV can no longer be identified. Therefore, there is hardly any signal difference between VV and VH at the flood date (Figure 8), which is confirmed by the low importance of Z-Score VV - VH. Compared to that the small increase in importance of the VV/VH can be explained by a small proportion of TFV in the forested areas due to limited penetration into the forest crown, as described  
Figure 7: Multitemporal behavior of the backscatter intensity for TFV areas for VV (dB), VH (dB), VV/VH (linear scale), and NDVI values in southern Greece/Turkey. The blue bars mark the analyzed date at the flood event. above. In the case of northern Greece/Turkey the importance increases, because the outliers were eliminated by using the Z-Score VV/VH feature. For southern Greece/Turkey on the contrary, using the Z-Score VV/VH the outlier reduction caused also an elimination of extreme values caused by TFV. This led to a drop of the importance in VV/VH in comparison to the Z-Score VV - VH.