# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 4 Results  
From the CSK catalogue, two Stripmap HIMAGE acquisitions datasets, covering the target regions, have been selected according to the wind and wave conditions provided by the nearest marine observing stations from SIMOP, RON and RMN. When observed data were unavailable, that preliminary selection step has been performed exploiting ERA-Interim Re-Analysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) [74]. The first selected dataset (Test Site I) contains 19 images, spanning from 2010 to 2013, whereas the second one (Test Site II) contains 38 CSK images acquired between 2009 and 2015. Remotely sensed and in situ data, provided as input to the COSMO-Beach processing chain used for validating the experimental results, are listed in Table 1.  
Taking into account that, in case of calm sea conditions, the surface of the sea appears as a very homogeneous region characterized by rather low amplitude values, mean and standard deviation of the amplitude values have been calculated in a sub-region, located offshore the examined beach, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Test Site I** & **Test Site II** \\ \hline SAR dataset & 19 CSK Stripmap HIMAGE & 38 CSK Stripmap HIMAGE \\  & LIDAR DEM 2008 1 m & LIDAR DEM 2008 1 m \\ DEM & (FOR PUGLIA 2000–2006) & (FOR PUGLIA 2000–2006) \\ Aerial photos & Maritime State Office (Apulia Region) & Maritime State Office (Apulia Region) \\ Waves & RON Wave Buoy (Monopoli) & SIMOP Wave Buoy (Taranto) \\ Tides & RMN Tidal station (Bari) & SIMOP Tidal station (Porto Cesareo) \\ GPS survey & 60 GPS transects spaced 10 m & 50 GPS transects spaced 10 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Remote sensed and field data selected for the experimental analysis over the two test sites. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CSK\_H4-05\_HH\_RD\_009** & **CSK\_H4-01\_HH\_RA\_009** & **CSK\_H4-02\_HH\_RA\_009** \\ \hline Instrument Mode & STR\_HIMAGE & STR\_HIMAGE & STR\_HIMAGE \\ Polarization & HH & HH & HH \\ Look Side & right & right & right \\ Pass Direction & D & A & A \\ Track & 9 & 209 & 209 \\ Beam ID & H4-05 & H4-01 & H4-02 \\ Off Nadir Angle & 30620 & 24130 & 24670 \\ Satellite ID & SAR1 & SAR1 & SAR1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: COSMO-SkyMed (CSK) dataset characteristics. and extracted from the filtered data, and their relationship with recorded wave heights has been investigated. The calm sea is characterized, as expected, by low backscattering coefficient values, whereas in rough sea conditions \(\sigma_{0}\) values grow considerably (Figure 3). Sea conditions can be likely distinguished using the average amplitude values: it was observed that when the backscattering coefficient exceeded a threshold value equal to \(-19\) dB, images could certainly be discarded, since acquired in conditions of rough seas with significant wave heights (\(H_{\mathrm{s}}\)), typically greater than \(0.5\) m. These results are in good agreement with results published in the recent literature for TSX constellation, i.e., the other X-band satellite SAR mission, which confirmed that the reflectivity of water surfaces in light wind conditions are less than \(-19\) dB [75]. This step would allow to select part of the SAR datasets for further analysis when field data or high resolution wave model data are not available. After filtering out noisy images, the selected SAR images were preprocessed and precisely geocoded thanks to the availability of high quality LIDAR surveys covering both areas of interest (AOI). Concerning the use of the interferometric coherence for coastal type classification, multiple acquisition pairs have been selected, with small temporal and geometric baseline in order to minimize temporal and spatial decorrelation noise sources. An average coherence map has been hence computed for minimizing time-uncorrelated fluctuations, allowing shoreline classification (Figure 4).