## 3 Results and Discussion  
During floods, timely and detailed situation reports are required by the disaster management authorities to locate and identify the affected areas and to implement the corresponding damage mitigation; this is the most delicate management category since it involves rescue operations and the safety of people and property [82; 83]. Many of the existing and future satellite missions and airborne platforms provide rich data with great potential for enhanced monitoring, measuring and mapping of floods [50; 84]. In this study, the flood dynamics and its impact were revealed, calculated, and analyzed using multisource data (L7, S1, and S2 EO data; DEM and public domain data). S1 satellite mission data are valuable for flood monitoring and damage assessment. However, in this case study, the outputs of water extraction using S1 data (Figure 7) represent solely numerous dispersed spots given that the image was obtained at the early stages of the phenomenon. Specifically, the transformation of the precipitation to runoff and thence to flood routing, requires a certain period that is related to the hydrological and geomorphological characteristics of the watershed. Thus, the major part of the study area was not under water at the S1 acquisition time at 07:03:18 a.m. of 21 May, because the phenomenon was at its early stages. Figure 7: The ’water’ image derived from the processing of S1 SAR image. The four calculated water indices based on the L7 image (MNDWI, TCW, DVW, and RSWIR) are presented in Figure 8. The evaluation results of the inundated area which were extracted from the water indices, utilizing raster calculator of ArcGIS [equation: Con (\"raster\"> = 0, \"raster\",0)], reveal that MNDWI (Figure 8a), TCW (Figure 8b) and RSWIR indices (Figure 8d) resulted in a higher accuracy of surface water mapping as compared to DVW (Figure 8c). The three indices calculated a flooded area of 5381.3 ha while by the DVW an area of 5125.2 ha was estimated. The flood extent map obtained from the four different water indices derived from L7 data has been compared and evaluated. The flood map depicted that during 20-22 May 2016 an area of 5381.3 ha was inundated in the study site (Figure 8). Specifically, from the MNDWI, TCW and RSWIR indices the estimated total flooded area was approximately the same (about 54 km\({}^{2}\)), while from the DVW there was an underestimation and it was assessed nearly at 49 km\({}^{2}\). As already mentioned, the L7 image was acquired on 23 May 2016, i.e., one day after the end of the storm event. Hence, there was a reduction of the total surface area covered by water in comparison to the maximum inundated area during 20-22 May 2016. Another limitation is that flood water depth, which is a critical factor for the assessment of floods impact on the agricultural production, cannot be directly estimated using remote sensing techniques [56]. Considering other possible limitations, such as trees or vegetation not entirely covered by water, two types of areas can be distinguished: (a) the flooded areas derived from the water indices and (b) the broader flooded areas (areas that were probably affected by the flood). Given these considerations the detailed DEM with 5 m spatial resolution was used to improve the inundation extraction results (using the MNDWI), to estimate the inundation depth as well as to assess the flood risk in the affected area. At a first step, a flood surface elevation model was  
Figure 8: The four implemented water indices. (**a**) MNDWI; (**b**) TCW; (**c**) DVW; and (**d**) RSWIR. calculated as it is described in detail in the methodology section and then the flood depth map was created by subtracting the DEM from the flood surface elevation model.