# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 1 Introduction  
Flooding affects societies, economies, and ecosystems worldwide and can have a devastating impacts. For the development of flood risk mitigation plans or disaster relief services, information about the flood extent in affected regions is an important information source for many institutions involved in crisis management, such as relief organizations, governmental authorities, and insurance companies [1; 2]. Numerous current studies focus on flood mapping using various algorithms that enable the detection of temporary open water [3; 4; 5]. Besides these areas, the extraction of temporary flooded vegetation (TFV) areas is essential for mapping the entire flood extent. TFV can be described as areas where water bodies occur temporarily underneath vegetation [6]. The disregard of these areas can lead to an underestimation of the whole flood extent. Ground-based observations or aerial vehicles are not always sufficient for the mapping of large-scale floods due to weather conditions, technical limitations, or high risks. Satellite remote sensing data, especially Synthetic Aperture Radar (SAR), allows one to overcome these challenges and covers an extensive area of the Earth's surface for a near-real time detection of large-scale flood events independent of daylight and weather conditions [7; 8] and is suitable for the detection of water underneath the vegetation areas. Smooth open water surfaces act as specular reflectors and are therefore characterized by low SAR backscatter values. These areas can be well differentiated from non-water regions showing higher backscatter values. In comparison, TFV shows highly complex backscatter behaviour, due to double- or multi-bounce interactions between the smooth open water surfaces and the vertical structures of the vegetation, such as trunks or stems. During the presence of water underneath vegetated areas, the SAR backscatter intensity values can significantly increase [9; 10; 11]. SAR-based techniques for the detection of TFV include simple approaches like manual thresholding [12; 13] or automatic thresholding [11; 14; 15], distance-based classification methods [16; 17], clustering algorithms [18; 19], decision tree [7; 20], or rule-based classification [21; 22]. Furthermore, more advanced approaches based on machine learning [23; 24; 25], fuzzy logic [11; 26], Markov Random Field modelling [14], or wishart classifications [27; 28; 29] are applied in dependency on the task, the availability of polarization modes, and phase information, as well as spatial or temporal resolution [6]. For the extraction of TFV, most of the approaches focus on a single-image, which represents only a snapshot of a current flood situation [12; 30; 31]. Other classification approaches use change detection techniques for the extraction of TFV, which allow one to detect potential changes in backscatter intensities [25; 26; 32; 33; 34; 35]. Thereby, the selection of the scenes acquired under dry and flood conditions highly influences the classification results. Furthermore, the increase of backscatter intensities, which is induced by TFV, may not clearly be separable from backscatter intensities from dry vegetation depending on its phenological stage [36]. Time series approaches can be more appropriate for the extraction of flood related classes by allowing the inclusion of seasonal or annual fluctuations of the backscatter. Furthermore, as a consequence of multiple observations of the same area, multi-temporal data can enable an improvement of the reliability for mapping TFV areas. The shape of multi-temporal radar backscatter profiles can be used to derive multi-temporal features enabling a more detailed extraction of flood-related classes [6; 7]. Only few studies address the detection of TFV based on multi-temporal satellite imagery, which either depends on the chronological order and regularity of satellite images [19; 37] or the use of a limited amount of SAR time steps [11; 34].