## 4 Conclusions  
Remotely sensed data are being increasingly used for flood extent mapping and damage assessment. Optical sensor data are readily available free of charge and they can be used with available well-defined, robust data processing techniques. However, as it was shown in this study, long revisit cycles of satellites can be a serious limitation for the investigation of flooding temporal dynamics using solely remote sensing data. Temporal evolution of the inundated areas can be critical for the assessment of the impact of floods in agricultural production, especially in the case of flash floods. 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. Accordingly, in this study case it was attempted to overcome these limitations by using multisource data (L7, S1, and S2 EO data; DEM and public domain data) to calculate and analyze flooding dynamics and their impact on agricultural production. Radar data provided by the S1 satellite mission are quite valuable for flood monitoring and damage assessment. However, at this case study, inundated areas delineation using S1 data resulted solely in numerous dispersed spots because the available image was acquired at the early/middle stages of the phenomenon, highlighting the significant limitation of six-day revisit cycle. Revisit time is expected to decrease with the launch of new missions and the integration of different satellites  
Figure 11: Topographic depressions classified according to their depth altitude to provide a measure of flood risk. constellations. This will likely make the water detection techniques for flood monitoring more reliable in the near future. The most suitable RS data in terms of acquisition time was an L7 image acquired on 23 May 2016, one day after the end of the storm event, which was a very fortunate situation considering that usually cloudy conditions prevail and flooded areas are not visible using optical data. Accordingly, in this case, a reduction of the total surface area covered by water in comparison to the maximum inundated area during 20-22 May 2016 is expected. The thorough analysis of the four different water indices calculated in this study based on the L7 image (MNDWI, TCW, DVW, and RSWIR), showed that all indexes were efficient at classifying pure water pixels; however, MNDWI and RSWIR indices appeared to be more suitable for delineating water bodies during floods. A detailed DEM with 5 m spatial resolution was used to fine tune the inundation extraction results, to estimate the inundation depth and to assess the flood risk in the affected area. The fine-tuned total affected area obtained by the flood depth map was a little smaller than the total flooded area obtained by RS because parts of the inundated area (according to RS) had actually higher or equal ground elevation than the flood water surface elevation. Interestingly, the total area having received compensations by ELGA is very close to the fine-tuned total affected area. This observation indicates that acquiring the RS image one day after the end of the event provided a sufficient time window for the estimation of areas that were inundated long enough to significantly affect the cultivations. Accordingly, considering temporal dynamics and flood depth instead of only the maximum extent of the flooded areas, may provide a better insight on the actual impacts. Furthermore, a direct correlation between the flooded area that was delineated with the present methodology and the area calculated and compensated by ELGA, based on field surveys, was observed. Finally, concerning the assessment of flood risk based on the analysis of topographic depressions it was found that the areas with higher estimated flood risk were visually correlated with the areas having greater flood water depths.