## 5 Sentinel-1: Opportunities
### Land Cover Map Production  
In this paragraph, we use the simulator presented in Section 4 to quantitatively evaluate how the land cover products developed in the frame of WARM-SAR can be replicated with Sentinel-1 data. As an example, we simulate the temporal evolution of the Laaba basin's water levels, by using an approach similar to that implemented in [34] for the simulation of flooded scenes. In particular, we modify the initial DEM (acquired when the basin is empty, see Section 2.2) reproducing the progressive filling of the basin with water-height steps of 0.5 m. We set the roughness (Hurst coefficient \(H\) and standard deviation at unitary distance \(s\)) and electromagnetic parameters (dielectric permittivity \(\epsilon\) and conductivity \(\sigma\)) of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameter** & **Terrain** & **Water** \\ \hline \(\epsilon\) & 4 & 40 \\ \(\sigma\) [ S/m ] & 0.001 & 1 \\ \(H\) & 0.8 & 0.75 \\ \(s\) [m\({}^{1\text{-}H}\)] & 0.1 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters used in the simulations  
Figure 3: Laaba’s storage volumes as a function of the corresponding surface areas. terrain and water-filled area to typical values (see Table 3). In Figure 4, we show the DEMs with different water levels that were given as input to the simulator. It is worthwhile to note that with respect to the DEM presented in [23], a local adaptive filter (see [35]) has been applied in order to reduce the noise due to the presence of vegetation on the boundary of the water surface. The simulated data have been processed in accordance with the approach presented in [19], where Level-1\(\alpha\) products were defined. In Figure 5, we show the simulated Level-1\(\alpha\) products for a scenario relevant to the Laaba reservoir, filled with different water levels. A simple classification rule, as explained in Section 2.4, allows the extraction of the water surface from the different acquisitions. It is worthwhile noting that, except for the area covered by water, the Level-1\(\alpha\) products exhibit a substantial balance between the test and reference intensity channels, providing an almost homogeneous cyan background. This is due to the fact that the simulator does not take into account the presence of vegetation, and the dielectric constant of terrains outside the reservoir was fixed to a constant value in all the simulated images. The contrast between water and terrain backscattering is very similar to that of the actual COSMO-SkyMed data. This result is supported by the analysis of the expected backscattering at the X- and C-bands. In Figure 6, we provide the backscattering coefficient of a typical rough soil, characterized by Hurst coefficient \(H\) = 0.8 and standard deviation at unitary distance \(s\) = 0.1 m\({}^{1-H}\), as a function of the incidence angles typical of remote-sensing applications (from 15 to 45 degrees). The curves have been evaluated with the small perturbation method (SPM) [31] for the HH polarization. The backscattering behavior is similar at the X- and C-bands. Similar results can be obtained at VV polarization. Figure 4: Laaba basin, 3D representation of the DEM used for SAR simulation. Water contour height: 341 m (**a**), 340.5 m (**b**), 340 m (**c**), 339.5 m (**d**), 339 m (**e**) and 338.5 m (**f**). A vertical stretching factor has been applied for visualization purposes. Figure 5: Laaba basin, simulated Sentinel-1 Level 1\(\alpha\) products. Water contour height: **(a)** 341 m, **(b)** 340.5 m, **(c)** 340 m, **(d)** 339.5 m, **(e)** 339 m and **(f)** 338.5 m. The spatial resolution is 5 \(\times\) 5 m.  
For water surface identification purposes, it is reasonable to analyze how the contrast between the water and the terrain backscattering is influenced by the frequency. In Figure 7, we plot the ratio between the backscattering coefficient of a rough terrain and of a water surface (see the parameters defined in Table 3) as a function of the incidence angle.