# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### The Floodwater/Permanent Water Mapping Algorithm
#### 3.2.4 Flooded Vegetation  
The key to detect flooded vegetation using backscattering data is the double bounce backscattering. It occurs when the portion of the radar energy that is specularly reflected by the ground hits the plant stalks that act like dihedral corner reflectors and return the incoming energy back to the radar sensor [7]. If calm water covers the ground, most of the incoming radar energy is specularly reflected and the intensity of the double-bounce effect increases; this may imply a significant increase of the backscatter with respect to that measured under non-flooded conditions, i.e., large values of \(\Delta\sigma^{0}_{VV}\). However, flooded vegetation does not generally have a clear and unique radar signature that can be easily detected. First of all, the double bounce is the prevailing effect of flooded vegetation if the penetration into the canopy is sufficient [8]. In dense vegetation, volume scattering hampers the detection of floodwater. This is the reason why pixels with \(NDVI>0.7\) are comprised in the exclusion mask. Moreover, even an increase of soil moisture and/or soil roughness (caused for instance by ploughing activities) produces large values of \(\Delta\sigma^{0}_{VV}\). Hence, simply searching for increases of the backscatter may cause commission errors. Recently, the use of cross-polarized data has been suggested to better detect the double bounce [11; 47; 48] considering that, in principle, the double bounce does not give rise to depolarization, so that small values of \(\Delta\sigma^{0}_{VH}=\sigma^{0}_{VH}\left(t_{2}\right)-\sigma^{0}_{VH}\left(t _{1}\right)\) are expected in flooded vegetation. The procedure adopted to identify flooded vegetation in AUTOWADE is shown in Figure 11. The output of the ISODATA applied to \(\Delta\sigma^{0}_{VV}\) is analyzed and the objects belonging to the cluster presenting the highest median of the values of \(\Delta\sigma^{0}_{VV}\) are selected. Then, to reduce the risk of making commission errors, only objects adjacent to areas classified as flooded are maintained, also considering that, as discussed in the Introduction, the objective is to fill the gaps typically present in flood maps. In practice, an object is maintained if it is connected to a buffer zone of two pixels arounds floodwater, created to account for imprecisions in the identification of the objects in the scene (e.g., due to residual speckle). Successively, the mean values of \(NDVI\) (denoted as \(\overline{NDVI}\)), \(\Delta\sigma^{0}_{VV}\) (\(\overline{\Delta\sigma^{0}_{VV}}\)) and \(\Delta\sigma^{0}_{VH}\) (\(\overline{\Delta\sigma^{0}_{VH}}\)) are computed for each selected object. Objects having \(\overline{NDVI}\leq 0.2\) are removed since in this case the backscatter increase is ascribed to an increase of soil moisture and/or roughness. Remaining objects are assigned to the class of flooded vegetation according to a fuzzy logic-based approach. Through a standard \(S\) fuzzy membership function (see Appendix C for more details about fuzzy logic), a degree of membership to the class of flooded vegetation is assigned, depending on the values of \(\overline{\Delta\sigma^{0}_{VV}}\), \(\overline{\Delta\sigma^{0}_{VH}}\), and the percentage (\(p\)) of the object's edge pixels connected to open water in the following way:  
\[d_{1}=S\Big{(}x=\overline{\Delta\sigma^{0}_{VV}},x_{1}=0\ \mathrm{dB},x_{2}=4\ \mathrm{dB}\Big{)} \tag{5}\]  
\[d_{2}=S\Big{(}x=\overline{\Delta\sigma^{0}_{VV}}-\overline{\Delta\sigma^{0}_{VH }},x_{1}=0\ \mathrm{dB},x_{2}=6\ \mathrm{dB}\Big{)} \tag{6}\]  
\[d_{3}=S(x=p,x_{1}=0\%,x_{2}=100\%) \tag{7}\]  
where \(d_{1}\), \(d_{2}\), and \(d_{3}\) are degrees of membership, \(x\) is the independent variable, \(x_{1}\) and \(x_{2}\) are the parameters of the \(S\) function (see Figure 11).