## 4 Materials and Methods  
In this work we exploit the potentials of both radar Sentinel-1 and optical Sentinel-2 images for a satellite-based quick detection and extent mapping of recent inundation/water raising events over Shkoder area. The input data for analyzing the three major events occurred during the last two years (2017-2018) are listed in Table 2. Moreover, one date in the summer period was also considered for evaluating a dry scenario of the study area and validating the applied approach. By observing the situation on one summer day (e.g., 14 August 2017), it is possible to have an overview over Shkoder area in a time without rainfall and to check the usual water level of the lake without any inundation event. The workflow of the satellite-based procedure carried out in this work follows two different methods for radar and optical ESA Sentinel data (Figure 3). Sentinel-1 radar data, acquired in microwave C-band (wavelength 5.6 cm) and in interferometric wide (IW) mode, were available in the ESA Sentinel Hub ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu))) data open access Hub as Level-1 GRD (ground range detected) products, which consist of focused SAR data projected to ground range using an Earth ellipsoid model ([PERSON] et al., 2017). Automatic data ingestion in ESA Hub allows us to search and download SAR acquisitions that match specific user-defined criteria, \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Date of Water Raising Events**} & \multicolumn{2}{c}{**Satellite Image Acquisition Date**} \\ \cline{2-3}  & **Sentinel-1 Radar Data** & **Sentinel-2 Optical Data** \\ \hline
14 August 2017 & 08/14/2017 & 08/15/2017 \\
1 December 2017 & 12/05/2017 & 12/06/2017 \\
3 March 2018 & 03/05/2018 & 03/08/2018 \\
11 March 2018 & 03/11/2018 & 03/11/2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satellite images from both Sentinel-1 and Sentinel-2 used for the extent analysis of the water raising events. Figure 3: Workflow of the satellite-based procedure. In the optical data analysis, SWIR means short-wavelength infrared while NDWI is the Normalized Difference Water Index. i.e., date of flood event. On these SAR data, a rapid flood mapping approach was performed on the sentinel application platform (SNAP) free software. Firstly, Sentinel-1 SAR images in intensity band VV polarization, which is found in literature to be the best polarization to analyze flooding extent [2; 31], are chosen and spatially subset. The cropped images are processed through radiometric calibration and speckle filtering, respectively for obtaining backscatter coefficient values and for reducing noise. A terrain correction is applied by using shuttle radar topographic mission (SRTM) 3 arcsecond data [32] to improve the geolocation accuracy of each scene by means of the compensation of the distortion caused by the LOS of the image acquisition. Then a semi-automatic thresholding procedure is used in order to determine flooded areas by means of threshold binning and unsupervised classification: The threshold is applied to the histogram of the filtered backscatter coefficient for generating water/land binarization and it is chosen to be the same for all the SAR images. The output is a flood extent map that discriminates surface water (permanent water and flood areas) from land/non-flood areas. Sentinel-2 sensor is equipped with multispectral instruments (MSI) capable of acquiring 13 bands information at different spatial resolutions (10 m, 20 m and 60 m) and its optical data provide 10-day repeat coverage of land areas. The main visible and near-infrared Sentinel-2A bands (named bands 2, 3, 4 and 8) have a spatial resolution of 10 m, while red and near-infrared bands (5, 6, 7 and 8a) along with its two shortwave infrared bands (11 and 12) have a 20 m spatial resolution; the coastal/aerosol, water vapor, and cirrus bands (1, 9 and 10) have a 60 m spatial resolution.