# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 3 Method
### SAR Image-Processing Algorithm for Flood Extent Extraction  
The most commonly used SAR flood extent mapping techniques include simple visual interpretation (e.g., [33]), image texture algorithm [29], supervised classification and various multi-temporal change detection methods (e.g., [18; 34]). However, one of the most commonly adopted methods for distinguishing between flooded and non-flooded areas is the interpretation of radar signatures (e.g., [21; 22]). The specular reflection of the radar signal is the main electromagnetic mechanism producing a high contrast in SAR images between flooded and non-flooded areas [14]. In general, floodwater that is covering the terrain is much smoother than the surrounding non-flooded land and reflects most of the impinging radar signal toward the specular direction, so backscatter is very low and flooded areas appear dark in shade [21]. This advantage of SAR data for flood mapping means computation efficiency for flood detection and monitoring algorithms that could make them suitable for supporting delineation of flood hazard scenarios requiring emergency management. Conversely, inundation mapping may be complicated by wind roughening and by vegetation emerging from water, producing high radar return that decreases the contrast between flooded and non-flooded areas in SAR images: for example, the scattering from a well-developed canopy becomes predominant and the presence of the underlying water is hardly detected by the radar [21]. In this light, the flood mapping procedure utilized in this study is basically a semi-automatic classification in which the integration of different rules is performed based not only on SAR but also on ancillary data, such as knowledge of land cover and information derived from a reference pre-flood SAR image. The utilized methodology for flood extent extraction follows the steps proposed by [18]. It is comprises (i) SAR image radiometric thresholding to extract the water body core, (ii) a region-growing approach in which the backscatter of image pixels is compared to check the similarity with the value of the nearest neighbor seed pixel, i.e., bodies of water, in order to extract all areas covered by water; finally, the utilized approach takes advantage of (iii) SAR change detection capabilities by exploiting two images acquired before and during the flood to monitor backscattering changes caused by the flood event. Change detection thus aims to remove from the flood extent map pixels that do not correspond to floodwater. To do so, only pixels that change their backscatter values significantly with respect to their baseline values are kept in the final flood extent map [18]. By using a couple of SAR images (one flooded and one unflooded) change detection can reduce the false alarm probability arising from shadow areas, which appear as dark as the flooded pixels. Only pixels that change their backscatter values significantly in the two images are kept in the flood extent. RGB composition is used for this end: pre-flood and post-flood images are co-registered, each to a single master scene, and are combined into a color image to identify the flooded areas better in comparison to permanent water and other classes. SAR intensity images were filtered through a Lee filter [35], also with an \(11\times 11\) sliding window pixel size, and then converted to a logarithmic scale. Subsequently, a numerical comparison of the different values of the two SAR images distinguishes between flooded and unflooded areas more easily. This scheme was used extensively in past literature studies (see [18; 23; 34]), but it can be complicated by a wealth of other factors influencing the actual backscattering level of the terrain, which include surface roughness and soil moisture for land areas, the presence of capillary waves for bodies of water, or the presence of rain during acquisition. For example, wind may cause capillary waves in free water surfaces of large size, which may then increase the local backscatter level of SAR images.