# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 1 Introduction
This mapping product embeds the evidence of the local vulnerability to flooding and highlights the key areas of concern where events of equal severity may happen again and cause similar (or even worse) impact, should no risk mitigation measures be put in place. With regard to the satellite data used, it is worth acknowledging the value of free and daily images at global coverage such as those provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for flood delineation and monitoring over large areas (e.g., [30]), as well as services to generate automatically near real-time flood maps using Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) imagery [31]. Well-known are the capabilities of such space-borne sensors to provide high temporal resolution, wide spatial coverage and distinctive discrimination at pixel level between flooded and non-flooded areas. However, these data do not satisfy the technical requirements of spatial resolution and independence from weather conditions for our study. Our scope is to assess flooding hazard and risk within Lima and along the course of the Rimac River flowing across the urban environment of the city using free data, document changes that occurred at the level of urban fabric, and delineate flooded areas in a geographic region characterized by high frequency of cloudiness. Therefore, we intentionally exploited SAR imagery collected by the European Space Agency (ESA)'s European Remote-Sensing (ERS-1/2) missions and the European Commission's Copernicus Sentinel-1 constellation over the periods 1997-1998 and 2017-2018 when flood events occurred. Despite the spatial resolution limitations already observed in the specialist literature [32] which could hamper the detection of flooded streets in densely built-up areas, these SAR images can provide a better observation solution than MODIS and VIIRS data in this environmental and landscape context. In this regard, the present paper aims to bring a novel contribution to the scientific literature of SAR-based assessment of flood hazard in Peru and, specifically, Lima. Recently [PERSON] et al. [33; 34] have analyzed a pair of Sentinel-1 SAR images over the city of Chosica (about 60 km from Lima) that was most affected by landslides and floods in March 2017. A basic pre-processing technique (radiometric calibration, speckle filtering, terrain correction) was applied prior to post-processing, the latter based on histogram analysis and masking for landslide zone identification. The results showed a significant number of sites with landslide and flood damage. However, this analysis relied on a single pair of SAR scenes only. In our study, instead, we extend this single-pair approach (quite common in rapid mapping of flood extent delineation [35; 36; 37]) to the whole archive of SAR images available over Lima, to create the three separate sets: \"pre-event\", \"cross-event\" and \"post-event\". In this sense, we also align with the efforts published in the recent literature to develop procedures making the best out of long time series of satellite data for real-world applications (e.g., [38]). Furthermore, to the best of our knowledge, no multi-temporal investigation of flood hazard in the hydrological basin of the Rimac River has been conducted so far with SAR time series. From an analytical point of view, while other more sophisticated techniques could have been used for both data mining and pattern interpretation (e.g., pixel-based flood mapping approaches including global and enhanced thresholding [39]; interferometric coherence analysis [40]; expectation-maximization for separation between similar and non-similar patterns [38]), we intentionally exploited SAR amplitude change detection and red, green and blue (RGB) color composites [41].