# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 4 Discussion
Therefore, the change detection methodology based on SAR data provides a robust and validated input to the risk classification presented in this study (Figure 18). While the above comparison reassures about the accuracy of our change detection approach, the classification of the observed \"Flooding\" (F) and \"No Flooding\" (NF) changes could not have been achieved reliably without the image analyst interpretation of the patterns and magnitude of the changes in the radar backscatter based on the integration with the ancillary and BTM data. As such, given that flood products of international programs (e.g., Copernicus Emergency Services) exploit different image-based approaches for flood detection, the methodology presented in this paper could complement them, provide value-added and be disseminated in national and international disaster risk reduction/emergency mapping frameworks. Figure 19: Comparison between (**a**) the Sentinel-1 ratio map of the areas inundated during the March 2017 flooding event and (**b**) the potential maximum flood extent (cyan polygon) produced by Copernicus Emergency Management Service (EMS) during the Risk and Recovery Activation EMSN-038 [44]. To assess the accuracy and reliability of our risk map (Figure 18), we compared it (Figure 20a) with the map of flooding susceptibility and fluvial erosion published by [PERSON] et al. [28] (Figure 20b). Both maps have a very similar distribution for the areas classified as at low risk, while they show significant differences for the areas classified as at high and medium risk. In particular, two main differences exist. First, some areas that in [PERSON] et al. 's map (Figure 20b) are identified as at medium risk, in our map are classified as at either low or high risk. Second, our map encompasses some larger areas at high risk along the river course that have been flooded. In the first case, it is very likely that the areas at higher risk classification in [PERSON] et al. 's map reflect the availability of more granular and higher detailed geological and topographic information to assess the flooding susceptibility than the open data (that were accessed for this research) could provide. In the second case, it is clear that, the information derived from the SAR images emphasizes the areas that have been already flooded and/or where urban changes that may increase risk have occurred. Therefore, our risk classification map embeds event-based information and knowledge of the impacts of urbanization that allow the improvement of the hazard mapping made solely based on geological factors. Therefore, the observed difference between the two maps should not be interpreted as a discrepancy, but instead as an objective demonstration of the benefits that information mined from SAR time series can bring in to improve hazard mapping products, as well as the value of high resolution geospatial information in flood hazard mapping. By knowing the temporal variability of the flooding events, it is possible to understand their impact at the basin, public safety and urban infrastructure level. If produced on a regular basis, such SAR-based mapping of urban and environmental changes would be a key instrument of disaster prevention. Early identification of areas potentially at risk of flooding is a prerequisite for the preparation of  
Figure 20: Comparison between (**a**) the flooding risk classification map generated in this research and (**b**) the map of flooding susceptibility and fluvial erosion published by [PERSON] et al. [28], to highlight (**c**) the mutual spatial overlap and risk classification differences. effective risk prevention and/or mitigation measures, with the effect of improving policies in the area, and consequently paving the way for the reduction of socio-economic impact that a flood would cause. For the case study of Lima, it is worth noting that the hydraulic risk map presented here was developed _a posteriori_, since the flood events had already happened. Therefore, it embeds information that was captured from SAR satellites during past events.