## 1 Introduction
Since the early 1970s, the Landsat program has provided us with the longest continuous global measurements of the Earth's surface, creating a historical collection unparalleled in quality, coverage and length. L7 is the seventh satellite of Landsat equipped with Enhanced Thematic Mapper Plus (ETM+); it operates on six multispectral bands with a spatial resolution of 30 meters, a thermal band at 60 meters and a panchromatic band at 15 meters. The European Space Agency (ESA) through the Copernicus program offers accurate, timely, and easily accessible SAR data of Sentinel-1 satellite system. Sentinel-2 (S2) is a new land monitoring and classification mission that provides high spatial resolution optical imagery to perform terrestrial observations with global coverage of the Earth's land surface every five days. S2 is equipped with the MSI (Multispectral Imager) sensor operating on 13 different bands at a spatial resolution of 10 (4 bands), 20 (6 bands), and 60 (3 bands) meters. Its data have significant use in studies related to land use/cover mapping and the quantification of related changes [40; 41]. Optical sensors are unable to procure clear images during cloudy weather conditions and therefore its application potential is limited in case of flood events accompanied by clouds. In such cases, SAR images are valuable, as several previous studies have been demonstrated by [PERSON] et al. [7]; [PERSON] et al. [42]; and [PERSON] [18], because they are not dependent on daytime and weather conditions, they provide data through clouds and hazy atmospheric circumstances and they can differentiate waterlogged and dry land [1; 7; 9; 18; 21; 42; 43; 44; 45; 46; 47]. Cloud penetration capacity turns out to be considerably significant for monitoring flood events as they customarily take place during periods of extended rainfall [18; 43; 48]. Several commonly used SAR flood extent mapping techniques include image texture algorithms [47], histogram thresholding [9], and various multi-temporal change detection methods [45]. [PERSON] et al. [49] showed that the detection of inundated areas can be optimized by selecting the most suitable polarization of the radar waves. It has been observed that usually HH polarization (H=Horizontal) provides a more efficient distinction of regions flooded than HV or VV (V=Vertical). VV polarized data were highly influenced by surface roughness conditions, but in some cases, they provide good results in flooded area delineation [18; 49; 50]. The main disadvantage of SAR data is that the resulting backscattered signal is a complex combination of effects which depend on incidence angle, vegetation density and orientation, relative water height, and wind effects. This could cause opposite backscatter responses for similar conditions, and the disentangling of such effects requires additional information [23]. Sentinel-1 (S1) is a synthetic aperture radar (SAR) mission of the Copernicus Program. The contribution of S1 to the application of flood mapping is due to the sensitivity of the backscatter signal to open water [40]. Through radar intensity imagery, in the absence of wind, the specular reflection of C-band signals over open water means that the signal is significantly lower than average. In the case of flash floods though, wind could be a problem. Furthermore, vegetation that is not entirely covered by water can be also a problem (for optical also) [9; 21; 40; 51; 52]. Having as a goal to overcome the inherent limitations of the various remote sensing (RS) techniques (cloudiness, trees or floating vegetation, long revisit cycles of satellites, etc.) [18; 22; 23; 53], various researchers investigated the use of additional spatial data such as land cover and elevation [54; 55]. As an example, [PERSON] et al. [46] developed image processing methodologies that allow for the extraction of water levels at any point of the floodplain in order to further improve the exploitation of SAR images for flood mapping that is crucial in hydraulic modeling and near real-time crisis management. [PERSON] et al.