# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 1 Introduction
Furthermore, the application of multi-temporal approaches is limited due to the low availability of respective SAR data in the past. SAR systematic and long-term data acquisition within short revisit times by satellite missions such as Sentinel-1 finally enables the usage of multi-temporal data. Although the pixel-based approach is the most commonly used method for the extraction of TFV [6], object-based approaches are emerging as an effective method for complex image classification [18; 30; 38; 39]. On the one hand side, the objects are suitable to map heterogeneous classes, such as TFV, if the spatial resolution of the SAR data is higher than the objects on the ground [6]. On the other hand, the grouping of pixels to objects with similar backscatter values in SAR images allows one to reduce speckle noise [10; 40]. Furthermore, objects enable the consideration of semantic or spatial contextual relationships, which helps to cope with heterogeneous feature such as TFV [6; 11]. Several segmentation techniques, which group pixels into meaningful or perceptual regions (objects) for the extraction of TFV, can be found in the literature, including multiresolution segmentation [24; 41], Mallat's discrete wavelet transform [42], Markov Random Fields [14; 43], and clustering approaches [10; 11]. The combination of segmentation techniques with SAR time-series data can be used to discover multi-temporal characteristics and patterns, allowing for the extraction of useful information from enormous and complex data sets [44] and empowering the derivation of time series features with the advantages of an object-based approach. In contrast to methods for the extraction of open water surfaces for flood detection [4; 45; 46], there is little research for the detection of the entire flood extent comprising TFV besides open water areas [11; 25; 29]. An example for automatic flood detection of open water in near-real time (NRT) by Sentinel-1 data is given by [PERSON] et al. [47]. In the framework of the Center for Satellite Based Crisis Information (ZKI) located at the German Aerospace Center (DLR), this method is designed to provide a fully automatic web-based Sentinel-1 Flood Service (S-1 FS) for rapid provision of open flood extent information for humanitarian relief activities and civil security issues worldwide [48; 49]. This S-1 FS is aimed to be extended and improved by the addition of TFV areas, which are derived by the developed time series approach within this paper. This article introduces a time series approach for the derivation of an entire flood extent with a focus on the extraction of temporary open water (TOW) and temporary flooded vegetation (TFV) using multi-temporal S-1 imagery. The objectives of this work are:  
1. To investigate the characteristics and patterns of SAR time series data and to show their potential regarding the detection of TOW and TFV;
2. To derive time series features, which are used as a basis for a time series approach focusing on the detection of TOW and TFV;
3. To classify the entire flood extent, including TOW and TFV in the analysed study area;
4. To improve the results of external approach DLR's S-1 FS [47] by supplementing temporary open water by TFV areas.