# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) Data Mining
#### 2.2.3 SAR Amplitude Change Detection  
Change in the surface roughness is the key to detect inundation using SAR data. Where the ground surface is covered with calm water, its low roughness exhibits an almost ideal specular reflection and the backscatter is therefore very low (i.e., dark pixels in the SAR image), in strong contrast to the scattering of natural surfaces in dry conditions (i.e., bright pixels) [67]. The most straightforward way to detect surface changes between two dates/acquisitions is the comparison of the SAR radar backscatter. This approach is only sensitive to changes that impact the backscatter of the object, and does not take the entire complex SAR information (i.e., also the signal phase) into account [42]. Because floods alter the surface roughness, the change from dry to inundated \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SAR Image Working Pairs** & **Pre-Event** & **Cross-Event** & **Post-Event** &
\begin{tabular}{c} **Total Number of SAR** \\ **Working Pairs** \\ \end{tabular} \\ \hline
1997–1998 & 1 & 4 & 4 & 9 \\
2017–2018 & 10 & 10 & 10 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SAR image working pairs made of ERS-1/2 and Sentinel-1 scenes over Lima, Peru. conditions can be easily captured with SAR amplitude change detection. It is worth mentioning that this is the most common method for flood identification. Many studies have demonstrated that SAR systems are suitable tools for floodwater mapping on bare soil or scarcely vegetated areas where water has its well-defined radar backscattering signature. However, there are some aspects that need to be accounted for when floods happen in densely built-up areas, and when medium resolution SAR images (such as ERS-1/2 and Sentinel-1) are used. In such environments, given the double bounce effect produced by buildings, and specular reflection of free-standing water, it may happen that flooded areas may exhibit stronger radar backscatter than the adjacent non-flooded areas [68; 69]. A further factor influencing the intensity of the radar backscatter is the inundation depth vs. the height of the building. Depending on the extent of the building facade not flooded, double bounce may predominate over specular reflection. Furthermore, it is known that, given the side-looking observation of space-borne SAR sensors, areas of urban ground surface may not be visible to the SAR due to radar shadowing and layover caused by buildings or taller vegetation and, therefore, urban flood extent may be not accurately detected [70]. These effects and interferences were taken into account during the interpretation of the change patterns, either increased or decreased radar backscatter, in the RGB composite and amplitude change detection maps that we generated for the periods 1997-1998 and 2017-2018. In particular, the effects of local topography and the properties of the urban fabrics were considered based on the 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM v.2) and urban land cover ancillary data (see Section 2.2.4). The following post-processing methodology for each SAR image pair listed in Tables 3 and 1-3 was applied according to the workflow described in [41]:  
* \"Coregistration stack\": which allows to overlay two images (input), i.e., the reference (master) and the secondary (slave), in order to ensure a perfect match between the pixels of the slave onto the geometry of the master reaching sub-pixel accuracy and, at the same time, preserving the original information contained in the slave [71]. * \"RGB Composite\": a multi-channel image generation, allowing for an immediate visualization of the changes in radar backscatter that occurred on the ground during the analysis period between the master and one or more slaves. Each of the R (red), G (green) or B (blue) channels (RGB) is allocated to either the master or the coregistered slave according to a specific combination of colors, thus obtaining a composite image where the different colors highlight different features.