# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 3 Methods
### Derivation of Time Series Features  
On the basis of the preprocessed SAR time series data, an analysis of the multi-temporal profiles was carried out to determine the characteristics and patterns of the flood-related classes (TOW and TFV) (see Section 4.1). Accordingly, the decrease and/or increase of the backscatter values in the SAR time series data at the analysed date of the flood event for each polarization and polarization ratio is essential information, which can be used to derive TOW and TFV. The extraction of flood-relevant time series characteristics is based on these multi-temporal characteristics and patterns.  
The backscatter values are influenced by different environmental conditions, whereby the intensity of the backscatter decrease or increase can vary within the desired classes. Influencing conditions such as varying land-cover classes, wind, water depth, or heterogeneity of the different vegetation types and their phenological stages can cause these variations [18; 60; 61]. Therefore, it is not always possible to compare the increase or decrease of the absolute backscatter values over time. In order to ensure the comparability, absolute backscatter values at the analysed date of the flood event were normalized over the time series for each pixel. This is achieved by the Z-transform of backscatter values and has been implemented by the following formula:  
\[\text{Z}=(\text{x}-\mu)/\sigma \tag{1}\]  
Hereby, x corresponds to backscatter values for the analysed date at the flood event, \(\mu\) represents the mean and \(\sigma\) the standard deviation of the backscatter values of a pixel over the time series. The transformed backscatter values allow spatial comparability of the data and are referred to  
Figure 4: Classification process chain for the extraction of temporary open water and temporary flooded vegetation based on SAR time series data. The deep orange arrows represent the pixel-based process part, while bright orange arrows show the object-based part of the process chain. \({}^{*}\) Global Urban Footprint (GUF), Height above Nearest Drainage (HAND) index, and SRTM Water Body Data (SWBD) as exclusion layers.  
hereinafter as Z-Score images. Because of the normalized description of the decrease or increase of the backscatter values, and the Z-Score in connection with the respective polarizations, their ratio (VV/VH) and their combinations (VV + VH, VV-VH) represent the essential time series features for the derivation of TOW and TFV. Mean value, median, and the standard deviation over the time series are considered as auxiliary information for the calculation of the Z-Score or as additional information in the later classification.  
A quantitative analysis of the time series features was carried out using the Random Forest (RF) method to investigate the potential of those to derive the TOW and TFV. Therefore, training data for two flood-related classes and a third class representing Dry Land areas were defined based on the reference data and analysed by RF. RF is an ensemble learning method based on the construction of a large number of decision trees using training data. The RF classifier is relatively robust against outliers and noise, which is important for spatially variable SAR data [62]. Besides performing a supervised classification, the application of RF algorithm allows one to derive the importance of different features. By determining their importance, one can identify which time series feature has the highest contribution to the classification results or is most reliable for the derivation of TOW and/or TFV. In addition, RF was used to analyse whether a single time series characteristic or combination of time series characteristics, and which one, allows for high classification accuracy for the two searched classes. As a result, redundant information is sorted out, and the information essential for classification is identified.