## 3 Results and Discussion
### Time Series Features--Transferability Analysis  
For the transferability analysis of the classification approach, several pixel-based and object-based classification runs were carried out for each study area and its subareas using the individual time series features. For each run, the most relevant time series feature (Z-Score VV + VH) was used for the derivation of TOW (Section 3.1). Simultaneously, each time series feature was used in each study area to derive the TFV, so that 20 pixel-based and 20 object-based classification products could be generated. For each result, an accuracy assessment was performed. Since the analysis of the different time series features refers to the TFV, the PA and UA of TFV were selected as indicators to represent the classification accuracy of the TFV. The accuracy values PA and UA for the individual study areas and the five analyzed time series features for pixel-based classification are shown in Table 7 and for object-based classification in Table 8. The relevant time series features that were identified by the Random Forest Algorithm (Section 3.1) are highlighted in dark green for the respective study areas for the PA and UA values. These tables show that different time series features are relevant for the individual study areas. The low PA values seen for northern Greece in both tables can be explained by the specific TFV type that dominates this study area, namely flooded forest areas. In this case, the penetration of the forest crown is limited by C-band data, whereby the water under the vegetation can only be partially detected. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Southern Greece/Turkey**} & \multicolumn{2}{c}{**Northern Greece/Turkey**} & \multicolumn{2}{c}{**China (\%)**} \\  & \multicolumn{2}{c}{**(\%)**} & \multicolumn{2}{c}{**(\%)**} & \multicolumn{2}{c}{**(\%)**} & \multicolumn{2}{c}{**(\%)**} \\ \hline  & Pixel-based & Object-based & Pixel-based & Object-based & Pixel-based & Object-based \\ \cline{2-6} DL—UA & 76.64 & 77.62 & 56.65 & 53.25 & 85.26 & 87.66 \\ TOW—UA & 86.53 & 86.87 & 97.58 & 98.23 & 79.73 & 85.65 \\ TFV—UA & 90.37 & 92.17 & 76.83 & 73.86 & 78.28 & 83.01 \\ DL—PA & 77.86 & 79.61 & 91.67 & 96.60 & 69.89 & 78.95 \\ TOW—PA & 85.94 & 86.38 & 91.02 & 91.67 & 90.47 & 91.14 \\ TFV—PA & 89.52 & 90.09 & 28.18 & 6.32 & 90.09 & 91.23 \\ OA & 84.26 & 85.17 & 81.47 & 79.59 & 81.37 & 85.84 \\ Kappa & 0.76 & 0.78 & 0.66 & 0.63 & 0.71 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy assessment for the pixel- and object-based classification for the individual study areas. Overall accuracy (OA) in %, Producer accuracy (PA) in %, User accuracy (UA) in %, and Kappa index (K). As described in the methodology, two quantities, mean value and CV, were derived based on the UA and PA values to provide a foundation for the identification of a single robust time series feature for the derivation of TFV types and for all study areas. If a time series feature reaches a high mean value compared to other time series features and shows at the same time the smallest CV, it is relevant for all study areas. Considering Tables 7 and 8, it is not apparent which time series feature would be suitable since the two statistical variables achieve the optimum at different time series features (highlighted in light green). The Z-Score VV shows the highest mean value (77.54%) for pixel-based classification and (75.57%) for object-based classification compared to other time series features, but Z-Score VV/VH shows the lowest CV value (0.18) for pixel-based and (0.19) for object-based. For a distinct identification of the most relevant time series feature, the two statistical variables have to be considered simultaneously. In Figure 13, the two quantities are plotted on two different axes. The larger the mean value and the smaller the CV, the more suitable is a time series feature for the extraction of TFV for all study areas.