## 2 Materials and Methods
### Methodology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & **Equation** & **References** \\ \hline NDWI—Normalized Difference Water Index & (\(\text{GREEN}-\text{NIR}\))/(\(\text{GREEN}+\text{NIR}\)) & [PERSON] 1996 [24] \\ \hline MNDWI—Modified Normalized Difference Water Index & (\(\text{GREEN}-\text{MIR}\))/(\(\text{GREEN}+\text{MIR}\)) & [PERSON] 2006 [26] \\ \hline NDVI—Normalized Difference Vegetation Index & (\(\text{NIR}-\text{RED}\))/(\(\text{NIR}+\text{RED}\)) & [PERSON] et al. 1974 [74] \\ \hline DVW—Vegetation and Water Index & NDVI –NDWI & [PERSON] et al. 2004 [39] \\ \hline RSVIR—Red and Short Wave Infrared Index & (\(\text{RED}-\text{SWIR}\))/(\(\text{RED}+\text{SWIR}\)) & [PERSON] \& [PERSON] 2004 [38] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of multispectral indices derived from the spectral bands of Landsat-7 image data. Figure 4: The four S2 images (False Color Composite: NIR, red, green band as RGB) used for the classification of the land use/cover of the area (a) 6 December 2015, (b) 04 April 2016, (c) 13 July 2016, and (d) 12 August 2016.  
the image band arithmetic was applied, putting as logical value (true) for values less than the chosen threshold and false for higher values, producing the final 'water' image. As far as it concerns the four S2 images, the atmospheric correction was applied to convert the top-of-atmosphere reflectance values (TOA) to corrected bottom-of-atmosphere reflectance values (BOA). For the atmospheric correction, ESA's Sen2 Cor plugin was used. Finally, bands 1, 9, and 10 were separated from the dataset. Then, the images were resampled using a bilinear method and were also subset to the study site extending [41; 66]. Then, a layer stack of the four images was made and a supervised maximum likelihood classification method was implemented, utilizing approximately 200 sampling points (Figure 4a-d). The resulted land cover classification was compared with the UAA data obtained by the Integrated Administration and Control System OPEKEPE [68] in order to cross check their accuracy. Following the delineation of the inundated areas using remote sensing, the detailed DEM with 5 m spatial resolution was introduced to improve the inundation extraction results, to estimate the inundation depth as well as to assess the flood risk in the affected area. The 5 m digital elevation model (DEM) is shown in Figure 5. As it can be seen, most of the affected area, and especially the area which is located between the levees, is almost flat with small elevation differences. The altitude gradually decreases from upstream (west and south) to downstream (east). The use of other freely available DEMs like SRTM or ASTER GDEM was also considered; however, the low vertical resolution of these datasets (1 m) and especially the low vertical accuracy in flat areas [77] were critical limitations for this application. To this end, a flood surface elevation model was created, based on the ground elevation at the boundaries of the inundated areas obtained by RS. Specifically, the elevation along the boundary lines of the identified flooded areas was sampled from the DEM every 5 m (Figure 6a). Then, a low pass filter with a 5 \(\times\) 5 pixel window was applied to smooth the obtained elevation data; the aim was to reduce the influence of outliers or extreme values which are due to possible errors in the inundated areas  
Figure 5: Detailed DEM of the affected area with 5 m spatial resolution. map, errors in the DEM, misalignment between the RS and the DEM pixels, etc. Then, the flood water elevation was spatially interpolated using a first-order local polynomial interpolation with barriers. An approximate spatial interpolation method was used instead of an exact method in order to further reduce the impact of outliers or extreme values in the obtained flood surface. Furthermore, the levees along the rivers were set as barriers in the spatial interpolation in order to consider the sharp changes of water surface elevation caused by their presence (Figure 5).