## 2 Materials and Methods
### Methodology  
The foundation for the analysis of the impact of time series features on the flood extent derivation is the SAR time series approach [8]. This method provides the opportunity to generate flood maps including the classes TOW, TFV and Dry Land (DL) based on different time series features. The main steps of the classification process for the generation of the flood maps are summarized below. The S-1 time series data presented in Section 2.1 provide a foundation for the generation of two independent multi-temporal layer stacks for each polarization and for each study area via an image preprocessing step. In addition, three further multitemporal stacks are generated, including the combinations of polarization (VV + VH, VV - VH, VV/VH). The ratio between VV and VH was calculated by using linear units, whereby for the elimination of outliers (e.g., due to speckle) only the values within the 5 th and 95 th percentiles were considered. Based on findings about characteristics and patterns for TOW and TFV [8], the time series features were determined using the z-transform of the backscatter values for each pixel over the time series and for the available multi-temporal layer stack. The z-transform allows the normalization of the backscatter values over the time series and ensures that the image elements of the analyzed flood scene are comparable with each other when considering the individual seasonal fluctuation of vegetation. The normalized time series features are termed as Z-Score. In relation to the polarizations, the time series features are Z-Score VV, Z-Score VH, the combination of polarization, Z-Score VV + VH, Z-Score VV - VH and the ratio Z-Score VV/VH, which represent the foundation for the derivation of TOW and TFV [8]. Based on training data, two time series features with the highest contribution for the derivation of the desired classes (TOW and TFV) were identified for each study area by using Random Forest Algorithm. These features were used in the last step of the classification approach. The classification of the flood-related classes was carried out based on pixels and objects. The K-means clustering algorithm [57] was applied for the generation of clusters using SAR time series data and the analyzed flood image. The resulting multitemporal and spatial cluster images were intersected with each other to combine multi-temporal and spatial information. The time series features, which are based on pixels, were merged with the segmented image by averaging the corresponding values within each object. The hierarchical thresholding approach is the last step in the classification process chain, which allows a successive derivation of the desired classes based on the image elements (pixels or segments). First of all, the permanent water surfaces are identified by the SWBD mask (see Section 2.1). On the remaining image elements, the TOW, TFV and Dry Land (DL) are then derived consecutively using the relevant time series features. The corresponding thresholds were determined automatically using the decision tree classifier and the above-mentioned training data. For further details and an in-depth explanation see [8]. The implementation of the entire approach was done in Python. The quantification of the classification accuracy of TOW, TFV, and DL was carried out using overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa index (K). In order to investigate the impact of time series features for the derivation of TFV and to show the potential of time series features for the transferability to different study areas and thus for the operational use, a statistical comparison of the classification accuracies based on different study areas and TFV types was performed. Thereby, a single time series feature was searched for, which allows the extraction of different types of TFV with high accuracy for all study areas. Besides the study areas described in Section 2.1, the study area in Namibia, which was analyzed in [PERSON] et al., [8], was also used for the investigation.