## 1 Introduction  
Flood events are the most frequent and widespread natural hazards worldwide and can have devastating economic, social, and environmental impacts [1, 2]. Precise and timely information on the extent of flooding is therefore essential for various institutions such as relief organizations, decision-makers of crisis management authorities or insurance companies [3]. Satellite Synthetic Aperture Radar (SAR) is particularly suitable for flood mapping, as this tool supports the large-scale, cross-border detection of the affected area independent of illumination and weather conditions [4, 5, 6]. The decisive advantage, however, is that in addition to open water surfaces,temporary flooded vegetation (TFV) can also be detected in dependency of system and environmental parameters [7]. TFV are areas where water bodies temporarily occur underneath the vegetation [8]. To avoid underestimations of the flooding, the derivation of both classes is essential to cover the entire flood extent. Smooth open water is characterized by low SAR backscatter values due to its specular surface. In comparison, TFV shows a significant increase in backscatter, especially in the VV (vertical/vertical) polarization, which is caused by the complex double- or multi-bounce interaction between smooth open water surfaces and the structure of vegetation (e.g., tree trunks, stems) [8; 9; 10]. Most approaches are based on the backscatter intensity allowing the detection of TFV by the identification of increased backscatter values compared to other objects (e.g., [4; 11; 12; 13; 14; 15]). Others utilize polarimetric decomposition and/or interferometric SAR (InSAR) coherence [7; 16; 17; 18; 19] to reduce confusion with urban areas or to minimize the misclassification of shadowed regions as non-flooded vegetation. Polarimetric decompositions, such as Freeman-Durden, Yamaguchi four-component, Cloude-Pottier, or m-chi decompositions, have all been demonstrated to be suitable for the extraction of TFV [7; 20; 21; 22; 23; 24; 25; 26; 27; 28]. However, the availability of full polarimetric data is often limited regarding the extent and temporal coverage. In the literature, various methods for deriving the flood extent based on SAR data can be found depending on the task, polarization modes, phase information, as well as spatial or temporal resolution of the satellite sensor [29]. Some of them include, for example, visual interpretation [30], histogram thresholding approaches [31; 32], image texture-based methods [33], Markov Random Field modeling [34], or Wishart classifications [17; 35; 36], which are mostly applied on single images. Change detection techniques in combination with algorithms, such as manual or automatic thresholding [21; 37] and fuzzy logic [38; 39], allow the extraction of potential changes between two images acquired under dry and flood conditions. Change detection methods are often carried out by using absolute backscatter values [40], which do not consider the chance intensity of backscatter values within vegetation. This can lead to classification errors in regions with high vegetation growth variability or with different vegetation types. A few advanced techniques, among other machine learning techniques [11], decision tree [41], or rule-based classification [13; 42] use satellite time series [4; 43; 44; 45; 46; 47] or multi-dates [11; 38; 48; 49; 50; 51; 52], which allow the inclusion of multi-temporal, -polarized or/and ancillary information for the extraction of temporary open water (TOW) and TFV classes. Thereby, seasonal or annual fluctuations of backscatter and multiple observations of the same area can be used to improve the reliability of mapping the flood extent or even the flood dynamics [43]. Moreover, the use of multitemporal approaches has been in the past limited due to the low availability of corresponding SAR data.