## 4 Conclusions  
The results of the study areas in northern and southern Greece/Turkey and in China show the potential of the method presented in [PERSON] et al., [8] using S-1 time series data to extract the total flood extent considering both temporary open water (TOW) and temporary flooded vegetation (TFV). In particular, different types of TFV were analyzed. The individual flood extent was determined by time series features, which represent the characteristics of both classes.  
This study confirms the results of the study of [PERSON] et al., [8], showing that the Z-Score VV+VH, which was derived based on the characteristics mentioned above, is the most relevant time series feature for the extraction of the TOW areas in all study areas. For the derivation of TFV, three different time series features were determined by the algorithm for individual study areas. This can be explained by the complex structure of the vegetation, the various analyzed vegetation types, and the dependency of the TFV on environmental conditions (e.g., vegetation type, soil moisture, topography), which differ in all study areas. Nevertheless, in the results for all study areas OA values reached between 80% and 87%, which were slightly higher even compared to the results in [8]. For the study area in northern Greece, the TFV areas could only partially be derived based on the C-band data due to the presence of forested vegetation, which reduces the penetration of the SAR signal. In both other study areas, TFV in high grassland/reed was successfully classified.  
The potential of the time series approach for transferability as a prerequisite for operational use was analyzed by comparing time series features with respect to their suitability for the derivation of TFV for all study areas simultaneously. For the comparison, statistical quantities were derived from the classification accuracies PA and UA of the TFV. For TFV, Z-Score VV, or Z-Score VV/VH appear to be the most relevant time series feature for pixel-based and object-based classification, respectively. Nevertheless, Z-Score VV is recommended for operational use based on pixel elements for the extraction of TFV as this allows improved differentiation to non-flood dates due to the more significant increase in the VV time series for TFV at the date of the flood compared to the VH time series. The Z-Score VV + VH is recommended for the extraction of TOW surfaces, because of the unanimity regarding the feature importance calculation for all study areas (Section 3.1). Since the time series features have been defined and no feature importance needs to be calculated, the user interaction is reduced to the initialization of the classification process and the computation time decreases.  
Figure 14: Comparison of object-based time series features as a function of mean value and coefficient of variation. The statistical values (mean value and coefficient of variance) were calculated on the basis of the PA and UA accuracy values.  
Overall, the approach can be used for irregularly occurring flood events, as well as for irregularly acquired S-1 images. It is flexible for individual applications depending on the vegetation and takes account of the seasonal changes by the use of multitemporal data. The presented SAR time series approach lays the cornerstone for automatic flood detection on a global scale, allowing the detection of the entire flood extent by supplementing the TOW with TFV areas.  
The concept of this study was developed by [PERSON], [PERSON] and [PERSON], while [PERSON] was responsible for the implementation, which included the preparation of data, generation and validation of flood classification maps, and methodological comparison of relevant features and their discussion.  
This work is funded by the Federal Ministry for Economic Affairs and Energy (BMWi), grant number 50 EE1338.  
The WorldView-2 imagery was kindly provided by European Space Imaging Ltd. (EUSI). The RapidEye imagery was kindly provided by Planet Labs Inc.  
The authors declare no conflict of interest.