## 2 Materials and Methods
### Methodology  
Optical remote sensing data collected by satellites are subjected to various deleterious effects related to sensor observation and solar illumination geometry, solar radiance/atmosphere conditions, detector calibration and settings, topography, as well as date and time of acquisition. It was necessary to achieve the comparison of images acquired from different sensors and at different times, to normalize the data sets to a standard set of conditions (normally reflectance values) [16]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Instrument** & **Image Code/Source** & **Acquisition Date** & **Use** \\ \hline Landsat 7 & ETM+ & LE71840332016144 NGS00 & 23 May 2016 & Flood monitoring \\ \hline Sentinel-1 & C-SAR & SIA\_W\_GRD\_1 SDV\_20160521704 3901_2016 & 21 May 2016 & Flood monitoring \\  & & 0521T043926\_011352\_01138F\_AFE4 & T07:03:18 & Flood monitoring \\ \hline Sentinel-2 & MSI & SIA\_OPERE\_PRD\_MSILIC\_PDMC\_20151206T14 & 6 December 2015 & \\  & & 1435\_R093\_V20151206T093115 & 20152106T093115 & \\  & & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Satellite** & **Transmission** & **Transmission** & **Transmission** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite images data sets. The intervening atmosphere between the satellite and the Earth's surface can degrade the quality of the remote sensing data, by altering the intensity of the spectral response of ground features received at the sensor from their actual spectral characteristics. Several algorithms are used for removing the effects of the atmosphere on the data and by which sensor radiance values are thereby converted to reflectance values. Various levels of input data are used to drive these atmospheric models, ranging from detailed atmospheric profiles acquired by radiosondes to standardized models representative of general latitudinal areas [16; 69; 70]. As atmospheric conditions can vary both spatially and temporally, standardised atmospheric models--such as that provided by QUAC atmospheric correction model in ENVI--is crucial for the atmospheric correction of the L7 image [71; 72]. Moreover, the treatment of different image data sets derived from different sensor systems in multiple combinations requires a high degree of geometric accuracy for proper feature information extraction and use in geographical information or environmental monitoring systems. Thus, an image to image registration was made between the two different satellite systems images, to achieve a well-built precision that will help the impending comparison of the data. In the present study, three different processing methodologies were followed for the three-different kinds of satellite data used (Figure 2). The final pre-processing step of the L7 ETM+ image included the gap filling that scan line corrector (SLC) failure creates. For that reason, Landsat gap fill plugin of ENVI was used to retrieve approximately 22% of data loss (Figure 3). This process deals with the implementation of a gap mask for each band that points the missing data in the scan gap and classifies these regions as 0 and the existing data as 1. Then, the digital number values of the L7 image have been converted to top of atmosphere reflectance and then to surface reflectance. This procedure was fulfilled utilizing the digital  
Figure 2: Flow-chart of the implemented methodology. elevation model (DEM) of the area, geographic longitude/latitude and the weather conditions that were present at the time of image acquisition. These latter parameters were automatically calculated by importing the images through the suitable import algorithm. The basic processing step concerned the calculation of the four water indices, the MNDWI, TCT, DVW, and the RSWIR (Table 2). Modified Normalized Difference Water Index enhances open water features while suppressing noise from built-up land, vegetation, and soil (Figure 4a).