# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 3 Procedures and Methodologies  
### SAR Sentinel-1 Data Pre-Processing  
The images were processed with the free software SNAP (Sentinel Application Platform) Tool version 6.0.0. [37], created specifically by ESA for the analysis of the data captured by Sentinel satellites. It is a very powerful program that has the right tools, Sentinel Toolboxes, to perform an accurate and thorough treatment of the images and subsequently run the analysis. In Figure 4, the sequence of the steps followed for the pre-treatment of the images is shown. Figure 3: Ribera Alta orthophotography dated 14 April 2018. (Orthphotographs source: CHE, 2018 [36]). In order to give continuity to the study, contribute to correct manipulation of images, and reduce the processing time, three subdivisions were made in each image, resulting in three areas of interest named after the most important towns and villages. These were zone 1--Novillas-Pradilla de Ebro, zone 2--Alcala de Ebro-Torres de Berrellen, and zone 3--Zaragoza-El Burgo de Ebro (Figure 1). The application of the right orbit helps to make the orbital data more accurate, improving geocoding and the subsequent processing results. With radiometric calibration, the value representing the radar backscatter intensity, in units of decibels, is taken for each pixel, and \(\sigma_{0}\)-bands are generated for the two polarizations that we provide, VH and VV [38; 39]. There is the possibility of obtaining other bands, but the \(\sigma_{0}\)-band is the one that provides the best separation between terrestrial soil areas and those with water [40]. Permanent water bodies have temporal heterogeneity regarding backscattering [41]. The backscatter intensity values are usually below \(-20\) dB and manifest themselves in dark, practically black tones. Applying the theoretical knowledge characteristic of the flat areas, the wave beams emitted by the sensor are reflected on the surface, which acts as a mirror, causing almost no return of energy towards the sensor; hence, its intensity value and representation in the radar image. There are surfaces that, at the scale of the measuring wavelength, behave as smooth and share almost identical scattering properties with water surfaces that can create \"false positives\" [42]. Speckle is represented in the images as mottled or granular noise. This causes degradation of the image quality and hinders its interpretation [43]. Mainly, noise is due to random oscillations of the signal returned to the sensor due to the interaction of the emitted wave with the rough terrain surfaces. There is no fixed procedure to suppress the speckle of RADAR images in flood extent mapping. The characteristics and configuration of each RADAR system, together with the conditions of the acquisition and characteristics of the land, establish the type and sequence of filters to be applied. The SNAP program has numerous filters, all of a statistical nature, to reduce this problem. A visual comparison was made with the filters Gamma Map, Lee, Refined Lee, and Lee Sigma, with different window sizes (Figure 5) in a small area, within the study area, which brings together flood areas, urban areas, and crop areas where the texture variability produced by the application of the different filters can be appreciated. The speckle filtering application does not modify the spatial detail and it is mostly used to achieve smoothing of the limits of the different forms represented, avoiding the loss of image details. The Gamma Map filter is based on the Bayes statistical analysis of the image and it works best for extensive areas, such as oceans, forests, or extensive fields [44]. The Lee filter uses statistical parameters, such as the mean and standard deviation, with a predetermined window size assessing different factors for smoothing. In homogeneous regions, such as flooded areas, the final pixel value is the linear average of neighboring pixels [45]. The Refined Lee filter provides a smoother texture in the image, minimizing radiometric and textural loss in the images [46; 47].