# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 5 Discussion
### Classification Results  
The comparison of the time series approach results with the reference mask provides good correspondence of 75.0% (OA, pixel-based) and 80.5% (OA, object-based) for the whole flood extent, comprising both classes TOW and TFV. [PERSON] et al. [37] achieved a similar OA (82.0%) using backscatter intensities of S-1 time series data for the detection of flooded areas, comprising TOW and TFV. However, a sequential time series is necessary for this method. Furthermore, the comparability of the validation results is limited by the differences between the study areas, including the vegetation types, the sizes of the individual classes, and the ratio in size between these classes and environmental conditions. In the following, the classification results are discussed in comparison to the validation image (Figure 10). It can be observed that the areas of the correctly classified classes (TOW = blue, TFV = green, Dry Land = brown) predominate; however, misclassifications are present. The misclassifications occur mostly for TFV in the classification image, which is marked as Dry Land in the validation image. Indeed, in these areas the VV and VH polarisations of the time series features Z-Score VV/VH have a significant difference during the flood event and are therefore classified as TFV. Nevertheless, these areas are marked as Dry Land in the validation image. This contradiction can be explained by the fact that the interpretation of optical data in relation to TFV is challenging, and water under the vegetation cover cannot always be clearly identified in the validation image. In addition, the temporal shift between the flood image (6 April 2017) and the validation image (8 April 2017) might also be a reason for the aforementioned misclassifications. Even so, no change in the flood extend could be observed; a receding flood extent (Figure 5) until the date of the S-2 acquisition could be possible. Thus, misclassified areas can indeed represent Dry Land; however, they can also constitute TFV areas in the S-1 flood image. Another significant misclassification is represented by yellow areas. For these areas, the interaction between water surfaces and vegetation may not be strong enough to cause an increase in the backscatter values in VV polarisation (10; 80; 81). Accordingly, there is no increase in the Z-Score VV/VH time series feature, so these areas were classified as Dry Land. The light blue areas represent confusion between TOW in the classification results and Dry Land in the validation image. These misclassifications may be caused by an insufficient decrease of the backscatter values in the VV polarisation at the analysed date of the flood event, which could not be detected by the generated threshold value. The transitional zones between TOW and dry areas have always been challenging to detect (82), because of the rapidly changing conditions during the flood event. Despite the misclassifications, the results show that for TOW 85.8% (UA) and 85.5% (PA) and for TFV 76.1% (UA) and 91.2% (PA) could be achieved. In addition, it was shown that the object-based classification compared to pixel-based classification can help to detect more precisely the areas affected by flood. This can be explained by the fact that the heterogeneity of the TFV in S-1 imagery is reduced by grouping pixels into objects providing a depiction of real objects on the ground. In the TOW area, misclassifications to Dry Land could also be partially reduced. They may have been caused by environmental conditions such as wind, which only affected a few pixels in an object. It also seems that speckle in the SAR data could also be reduced by using objects for classification (Figure 10). However, it should be noted that an additional step to calculate objects must be performed. The results of previous studies also confirm that object-based classification reaches higher accuracies in comparison to pixel-based classifications (27; 41).