## 3 Results and Discussion
### Classification Results  
The classification of the study area in southern Greece/Turkey was carried out based on the S-1 time series stack (October 19, 2014-December 25, 2016) containing the flood image (April 4, 2015). This classification was validated using the S-2-based reference mask (April 5, 2015). Figure 10a shows the pixel-based classification result of the time series approach, which comprises the classes POW, TOW, TFV, and DL, while Figure 10b shows the object-based classification result. For visual comparison, the validation mask is shown in Figure 1d. It is noticeable that the areas of the classes TOW, TFV, and DL contain more small structures of the other classes in the pixel-based classification compared to the object-based classification. The usage of objects reduces this noise; however, it also can lead to the loss of details and thus information. In general, the use of pixel- or object-based classification depends strongly on the kind of the landscape under investigation. In the case of flooding in vegetated areas, a single, isolated pixel could contain water ponds and so differ from its neighboring pixels. Due to limited visibility in optical data, these areas could not be identified during the generation of the validation mask. For the study area in northern Greece/Turkey, the classification is based on the S-1 time series stack (October 19, 2014-December 25, 2016), with the flood image being acquired on March 12, 2015. Validation of this classification was performed using the S-2-based reference mask (March 15, 2015). Figure 11a shows the pixel-based classification result for the study area of northern Greece/Turkey, which comprises the classes POW, TOW, TFV, and DL, while Figure 11b shows the object-based classification result. Compared to the validation mask (Figure 1c), both pixel- and object-based classification show only a low representativity of the class TFV. This can be explained by the low penetration of the vegetation (deciduous forest (Figure 2a)) by the C-band data, which is reduced in this case [8]. In addition, occasional TFV areas in the classification can be identified in DL of the validation mask, especially in the pixel-based classification. These could be small areas of water (ponds or temporary water accumulations). In combination with vertical structures of the agricultural areas or grassland, the double bounce effect can also be produced resulting in an increase in the backscatter values at the date of the flood. These areas were also classified as TFV, though they could not, however, be identified during the generation of the validation mask due to limited visibility in optical data. Figure 10: Pixel-based classification result (**a**), object-based classification result (**b**) for the study area in southern Greece/Turkey. The classification for the study area in China was performed using the S-1 time series stack (October 19, 2016-February 23, 2018). The analyzed date of the flood is June 28, 2017. The validation for this classification was performed using the S-2-based reference mask (June 27, 2017). Figure 12a, b show the pixel- and object-based classification results with POW, TOW, TFV, and DL classes. The validation mask used is shown in Figure 4c. The comparison between the classification results and the validation mask reveals the occurrence of TOW in DL. DL also occurs between the permanent water surfaces and the TOW areas. On the one hand, the confusion between these two classes can arise due to small areas of water occurring in the agricultural areas. On the other hand, the duration of a flood event can lead to an accumulation of S-1 images, containing the inundation and a decrease of images without flooding. This can cause a higher fluctuation range and a lower mean value when generating the time series features, which in particular can lead to confusion with DL, as the areas between permanent water and TOW demonstrate. The combination of these two conditions makes the above-mentioned confusions between the classes possible.