# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 4 Results
### Relevant Time Series Features  
Relevant time series features were determined for the derivation of the flood-related classes based on trainings data, which were taken from the reference data. The total number of training pixels for each of the three classes is 15,559 (TOW), 15,563 (TFV), and 11,550 (Dry Land). The importance of time series features for each class was implemented by RF (see Section 3.2). The analysed time series features include Z-Score for the VV and VH polarizations, Z-Score for the ratio between VV and VH, and two Z-Score combinations VV + VH and VV-VH.  
The training data were used to visualize the distribution and separability of the classes with respect to the derived time series features in a histogram. Figure 7 shows histogram distributions of the training data for each aforementioned class and time series feature. The separability between the TOW and other classes is clearly recognizable for Z-Score VV and Z-Score VV + VH. The overlap between the histograms of the classes TOW and Dry Land is strongly pronounced for Z-Score VV + VH and Z-Score VV/VH. In addition, there is an overlap to TFV by using Z-Score VH. The separability of TFV from other classes can be observed for Z-Score VV, Z-Score VV-VH, and Z-Score VV/VH. In particular, Z-Score VV/VH appears as a suitable time series feature to distinguish the TFV from other classes.  
The RF algorithm was used to quantify the importance of time series features for the TOW and TFV classes (see Section 3.2). Thereby, the contribution of each time series feat  
Figure 7: Histogram distributions of training data for the classes TOW, TFV, and Dry Land for individual time series features. Z-score VV (**a**), Z-score VH (**b**), Z-Score VV + VH (**c**), Z-Score VV-VH (**d**), and Z-Score VV/VH (**e**).  
the derivation of the respective desired classes. Taking into account the training data, 2000 estimators and a maximum tree depth of 500 were defined to identify the importance. These parameters were used to exclude randomness from the results. Figure 8 shows the importance of time series features for the classes TOW and TFV. It can be observed that for TOW, Z-Score VV, and Z-Score VV + VH, there are time series features with the highest contributions of 34.9% and 34.4%, respectively. The time series feature with the highest contribution (43.1%) for the TFV class is Z-Score VV/VH.  
The time series feature with the highest contribution does not necessarily represent the highest possible classification accuracy. Therefore, it is analysed whether a single feature or a combination of multiple features would result in the highest possible classification accuracy for the respective desired class. For this purpose, a Random Forest classification was performed based on aforementioned training data. The use of the time series features with the highest contribution and the two highest contributions for the classification resulted in an overall accuracy (OA) of approximately 98.0% for the TOW class. In comparison, the combination of all time series features resulted in only 93.1%. Equivalent for the TFV class, the time series feature with the highest contribution achieved an OA of 98.2%, whereby the combination of two or all of the time series features resulted in lower accuracy (97.5% and 96.2%).  
RF algorithm's importance shows that Z-Score VV represents the most reliable time series feature and the feature with the highest contribution for the extraction of the TOW. For the extraction of the TFV, Z-Score VV/VH represents the most reliable time series feature and the feature with the highest contribution. The analysis of the OA with the RF algorithm also showed that the combination of all time series features is less accurate than the classification accuracy based on the time series feature with the highest contribution for the classes TOW and TFV. Based on these findings, the time series features Z-Score VV and Z-Score VV/VH were used to derive classes TOW and TFV by means of the time series approach.