# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 5 Discussion  
### Multi-Temporal Characteristics and Time Series Features  
The analysed characteristics and patters of SAR time series data clearly show a decrease in backscatter values for TOW and an increase in backscatter values for TFV at the analysed date of the flood event (Figures 5 and 6). Thereby, the decrease indicates the occurrence of open water surfaces, which are characterized by low backscatter values, in which the emitted sensor energy is reflected away by the specular water surface [71]. The increase can be explained by the double- or multi-bounce interactions between the specular water surface and the vertical structures of the vegetation [9; 10; 11]. In many previous studies, similar behaviour of the backscatter values for the classes TOW and TFV is described [11; 20; 25]. These characteristics and patters in backscatter values are a prerequisite for the detection of the flood-related classes. The length of the decrease or increase of the backscatter values depends on the duration of the flood, the spatial extent, and the revisit time of the satellite. It should also be noted that backscatter values can be strongly influenced by the environmental conditions (see Section 4.2). For example, wind or heavy rainfall can roughen the water surface and reduce or even erase the typical decrease in backscatter values during the flood event [4; 9]. The increase in backscatter at the date of the flood can also be strongly influenced by the interaction between sensor  
Figure 14: Accuracy assessment results based on the S-IFS and its improvement (S-IFS + TOW and S-IFS + TOW + TFV). Figure 15: Overall Accuracy (AO) and Kappa Coefficient for each classification run based on a decreasing number of images. characteristics (wavelength, angle of incidence, and polarisation) [6] and environmental conditions, such as aboveground biomass [72; 73; 74] or the relation between water level and plant height [10]. Such external environmental conditions can have a limiting impact on the methodology presented here. In previous studies, absolute backscatter values are applied for the extraction of flood-related classes [10; 37]. This can lead to underestimation or overestimation of the TFV areas and limits the use of the method. These disadvantages can be compensated by the normalisation of backscatter values over the time series, which ensure the comparability of the increase or decrease in backscatter values for each image element independent of the different types of phenological development of the vegetation. Moreover, the developed method enables simple management of SAR time series data without the use of extensive methods to describe the seasonality or the typical vegetation conditions. In comparison to other studies [19; 37], which are based on the dependency of the sequential dates order of the multi-temporal SAR data, the technique presented in this paper can deal with non-sequential or irregular times series data. Sentinel-1 scenes acquired within a period of two years, which are containing the flood event, the flood event made it possible to distinguish the natural range of variation for the analysed vegetation from the changes induced by a flood event. The decrease and the increase of the backscatter values in SAR time series data were used for the extraction of time series features. Therefore, not only the polarizations VV and VH were applied, but also their combination. By combining polarizations, the effects, such as different sensitivities of the polarisations to objects, can also be intensified, e.g., by mathematically induced stretching of the data [75; 76; 77]. The intensity of the amplification was quantified using the RF method (Figure 8). For the extraction of TOW, Z-Score VV was determined by a RF classifier as the most reliable time series feature, having the highest contribution in comparison to the VH based time series features. While this finding matches the results of other studies [4; 47], some studies [25; 37] prefer VH as a basis for the classification.