Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study

Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering 7 × 7 window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre- and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.


Introduction
A river flood occurs when the river flow can no longer be contained within its bed, and over spills its banks. Flooding is a natural and regular reality for many rivers, caused by any pulse of overflowing water (heavy rainfall, peak seasonal rains, or seasonal snow melt) that overwhelms a river channel, which supports the most naturally dynamic ecosystems, modeling soil and re-distributing nutrients in case; (2) selection of the optimal processing and correction options (orbital parameters, radiometric, noise reduction-speckle, and geometric geometric) to perform an accurate and thorough treatment of Sentinel-1 SAR images, in order to optimize the detection and analysis of flooded areas; (3) application of two detection algorithm approaches (RGB-Red, Green, Blue color model -composition and calibration threshold technique) of free water surfaces to derive flood cartography from SAR images, which permits the delimitation of non-permanent water surfaces; and (4) final comparison and analysis between the flooded area maps derived from SAR images, the official orthophotography provided by the Ebro Hydrographic Confederation (CHE, acronym in Spanish), and flood risk areas by the National Flood Zone Mapping System (SNCZI acronym in Spanish) provided by the Spanish Ministry for the Ecological Transition (MITECO, acronym in Spanish).

Study Area and Data Source
The Ebro river runs through seven autonomous communities and its hydrographic basin is one of the largest in the Iberian Peninsula. Its great extent favors geological, topographic, climatic, and water balance variability. The study area was focused in the middle basin of the Ebro, from the villages of Novillas to El Burgo de Ebro, and this entire section of the Zaragoza province ( Figure 1). It is characterized by an average and very smooth slope; it has a high sinuous index with a meandering morphology and wide floodplains [18]. Irrigated fields, such as wheat, alfalfa, corn, barley, or rice, and permanent crops, such as vineyards, olive groves, or apple trees, are prevalent on the banks of the river [19].
Water 2019, 11, x FOR PEER REVIEW 3 of 25 flooded areas; (3) application of two detection algorithm approaches (RGB-Red, Green, Blue color model -composition and calibration threshold technique) of free water surfaces to derive flood cartography from SAR images, which permits the delimitation of non-permanent water surfaces; and (4) final comparison and analysis between the flooded area maps derived from SAR images, the official orthophotography provided by the Ebro Hydrographic Confederation (CHE, acronym in Spanish), and flood risk areas by the National Flood Zone Mapping System (SNCZI acronym in Spanish) provided by the Spanish Ministry for the Ecological Transition (MITECO, acronym in Spanish).

Study Area and Data Source
The Ebro river runs through seven autonomous communities and its hydrographic basin is one of the largest in the Iberian Peninsula. Its great extent favors geological, topographic, climatic, and water balance variability. The study area was focused in the middle basin of the Ebro, from the villages of Novillas to El Burgo de Ebro, and this entire section of the Zaragoza province ( Figure 1). It is characterized by an average and very smooth slope; it has a high sinuous index with a meandering morphology and wide floodplains [18]. Irrigated fields, such as wheat, alfalfa, corn, barley, or rice, and permanent crops, such as vineyards, olive groves, or apple trees, are prevalent on the banks of the river [19]. The study of the great historical floods of the Ebro river, such as those that occurred in 1643, 1775, 1871, or 1961, and more recent ones, such as those of 2003 or 2015, provides information on the changing dynamics of the river's course and how the Ebro has tried to consolidate in the flood zone, encountering man-made barriers to prevent the flooding from occurring [20]. The Ebro is one of the most regulated rivers in the Iberian Peninsula and suffers from frequent flooding [21]. The data The study of the great historical floods of the Ebro river, such as those that occurred in 1643, 1775, 1871, or 1961, and more recent ones, such as those of 2003 or 2015, provides information on the changing dynamics of the river's course and how the Ebro has tried to consolidate in the flood zone, encountering man-made barriers to prevent the flooding from occurring [20]. The Ebro is one of the most regulated rivers in the Iberian Peninsula and suffers from frequent flooding [21]. The data supplied by the Hydrological Information Systems (SAIH, acronym in Spanish), in operation since the end of 1997 at the CHE, has facilitated the correct management of the reservoirs of the basin and has helped to control the floods since then. They provide, among other data, information on the levels and water flows in the main rivers and affluents, as well as the level of dammed water in the reservoirs, through sensors or stations located therein. Table 1 shows the maximum values in the last three large floods of the Ebro River in 2003, 2015, and 2018 with data provided by the gauging stations of Castejón and Zaragoza [22]. The floods of 2003 and 2015 left maximum flow values higher than those of 2018. This is due, in part, to the fact that after the flood of 2015, measures were taken to improve the safety of the populations closest to the channel, restore the riverbed, create new deviation channels, and construct new containment infrastructure [23]. The winter of 2018 was one of the rainiest since records were first kept. In late February and in March, there were considerable episodes of rain and snowfall, accompanied by wind, throughout the Iberian Peninsula. This produced a high accumulation of water in the Ebro River Basin, especially in the upper and middle sections, and snow at the headwaters of the rivers. This was aggravated by the fact that extreme values of soil moisture had been reached, saturating it and increasing the runoff. Days before the event, some reservoirs in the basin had been preventively drained. Between 7 and 12 April, an extraordinary flood, which is the object of study of this article, took place. This was due to persistent, widespread, and very significant rainfall, and also snowfall at heights between 800 and 1600 m, with lower temperatures that were unusual for this time of the year. To this was added, in the previous days, snow thawing in the Pyrenees in the early weeks of spring. In the first half of April, it rained in the basin more than three times the normal amount [24].

Hydraulic Parameters Characteristic of the Flood
To know the maximum flow dates during the flood and to confirm the adequacy of the dates of the Sentinel-1 SAR images, data were taken from some of the gauging stations that exist along the course of the Ebro River ( Figure 2). The values at the stations of Castejón and Tudela, in the province of Navarra and upstream of the study area, were taken into account, despite not being within the delimited study area. These give us an idea of the development of the flood and how the maximum flood points have been moving along down along the Ebro axis with time.

Sentinel-1 SAR Data
Synthetic Aperture RADAR (Radio Detection and Ranging) is a powerful active remote sensing technology used for several applications, including flood monitoring. RADAR are active systems that illuminate the Earth's surface and therefore, images can be acquired by day during any lighting conditions, or at night, in the total absence of sunlight. In addition, these images are not affected by cloud cover, fog, or smoke because the RADAR signals have the ability to penetrate these covers [25,26].
Under the weather conditions in which floods occur, these types of images are the most suitable for study. Active radar systems incorporate an antenna that generates and transmits electromagnetic pulses (between 0.3 and 300 cm) to the Earth's surface, and after interacting with the objects, the echoes of the backscattered signal are collected and amplified by a receiver at certain frequencies and polarization modes. Currently, there is a wide variety of SAR missions that have improved spatial resolution, bands, revisiting times, and polarization modes that have increased the monitoring potential [27].
The backscattering coefficient, or sigma nought (σ 0 ), is the measure of the reflective strength of a radar target, defined as the per unit area on the ground, and is defined as the normalized measure of the radar return from a distributed target [28]. The amplitude and phase of the backscattered signal depends, among others, on the land surface properties, including the geometry, vegetation, roughness, and dielectric constant [29,30].
Continental waters, due to their low or null roughness in the absence of waves, behave like a specular surface, reflecting the radar return signal in a direction opposed to the sensor position, and therefore show very low backscatter values (negative) with respect to other land covers. This strong contrast in backscatter values allows the precise delimitation of flooded areas with SAR images. When a rough terrain has slopes with an angle that is more steep than the sensor depression angle, it generates shadow regions that appear without signal, very similar to the flooded areas, which can easily be confused. As flood areas tend to be flat, this effect is minor and is usually correctly delimited with detailed topographic information [31].
In this study, it is been used Sentinel-1 SAR images because of their features, configuration, and the free data set available online from Earth Observation (EO) data resources. Sentinel-1 SAR datasets, coming before and after the event, were downloaded via the Copernicus Open Access Hub platform [32]. The Sentinel-1 sensor is part of an ESA (European Space Agency) satellite constellation. It is composed of Sentinel-1A, launched on 3 April 2014, and Sentinel-1B, on 22 April 2016. Both satellites have a useful life of 7 years and a 12-day orbital repetition cycle (6 days between them). These temporal limitations of image capture mean that we did not have images of the day of the highest level of the Ebro at the study points. The available image that was closest to the moment of maximum flood was from 13 April 2018 and it was the one used for the development of the study. Table 3 shows the characteristics of the two images used in the study. Usually, depending on the methodology used, the pre-event image serves as a reference and record of permanently flooded areas, while the post-event image serves to establish the differences with the initial image [33]. Each image has a coverage range of 250 km with a 5 × 20 m resolution. The images are Level-1 Ground Range Detected (GRD) products; this means that they were detected, multi-looked, and projected to the ground range using an Earth ellipsoid model [34]. The acquisition mode is Interferometric Wide Swath, and the images have a high resolution; the sensor pass is ascending, with incidence angles between 29 • and 46 • , and dual polarization, VV (Vertical-Vertical) and VH (Vertical-Horizontal). The study area is covered by VV and VH only according to the observation scenarios shown in Earth Observation Data Center for Water Resources Monitoring (EODC). In the free modes of IW (Interferometric Wide) Sentinel-1, only VV + VH polarization over land is available [35].

Additional Ebro Hydrographic Confederation Data
The Hydrological Planning Office of the CHE has been tracking air flights during flood episodes since the extraordinary flood that took place in February 2003. Flights are scheduled according to the time of the maximum flood peak as long as weather conditions allow it. Through these flights, oblique and vertical aerial photographs, as well as videos, are obtained, all in high resolution. This information provides the basis for the cartographic restitution of orthophotographs of the National Aerial Ortophotography Program (PNOA, acronym in Spanish) included in the General Spanish National Plan for Territory Observation (PNOT, acronym in Spanish) and to obtain, by digitization, the flood areas corresponding to each flood. This favors a greater understanding of the water behavior of the Ebro river over time.
For the study area, a vertical aerial photograph corresponding to a part of the section that the CHE called "Ribera Alta" was used ( Figure 3). It covers a region that goes from the village of Novillas to Alagón and was captured on 14 April 2018.

Additional Ebro Hydrographic Confederation Data
The Hydrological Planning Office of the CHE has been tracking air flights during flood episodes since the extraordinary flood that took place in February 2003. Flights are scheduled according to the time of the maximum flood peak as long as weather conditions allow it. Through these flights, oblique and vertical aerial photographs, as well as videos, are obtained, all in high resolution. This information provides the basis for the cartographic restitution of orthophotographs of the National Aerial Ortophotography Program (PNOA, acronym in Spanish) included in the General Spanish National Plan for Territory Observation (PNOT, acronym in Spanish) and to obtain, by digitization, the flood areas corresponding to each flood. This favors a greater understanding of the water behavior of the Ebro river over time.
For the study area, a vertical aerial photograph corresponding to a part of the section that the CHE called "Ribera Alta" was used ( Figure 3). It covers a region that goes from the village of Novillas to Alagón and was captured on 14 April 2018.

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.

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. 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-Alcalá de Ebro-Torres de Berrellén, 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 σ0-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 σ0-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]. The Lee Sigma filter uses a statistical distribution of the digital levels of a given area window chosen by the operator and estimates what pixel should be considered [48]. Taking into account mainly the topography, the geometry of the urban areas, the polarization, and the need to delimit those areas with smaller intensity values, the Lee filter with a window size 7 × 7 (m) was chosen as being most appropriate for the study.  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-Alcalá de Ebro-Torres de Berrellén, 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 σ 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 σ 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]. The Lee Sigma filter uses a statistical distribution of the digital levels of a given area window chosen by the operator and estimates what pixel should be considered [48]. Taking into account mainly the topography, the geometry of the urban areas, the polarization, and the need to delimit those areas with smaller intensity values, the Lee filter with a window size 7 × 7 (m) was chosen as being most appropriate for the study.  The last common step in the pre-treatment of images is to perform a correction of the terrain and orthorectification. This mainly eliminates distortions due to changes in the topography and the angle of incidence with the ground with respect to the nadir. The geometric calibration used in this study was range doppler terrain correction and the digital elevation model (DEM)-SRTM-3Sec.

SAR Sentinel-1 Data Processing
Many authors have used various algorithms for the detection of non-permanent water surfaces. Among these approaches, we can highlight the supervised classification [49], automatic non-supervised classification [46,50], RGB combination [38,51], or the detection or calibration threshold technique [41,[52][53][54][55][56][57]. In this study, the methodologies used were RGB composition and the calibrated thresholds technique. Before the application of any techniques and to perform a multitemporal analysis, it is necessary to unify in a single file the different bands of σ 0 of the images before and after the event using a stack.
The RGB composition is an appropriate method to visualize multi-temporal modifications and facilitates the detection of changes on the terrain surface through a temporal color image [58]. It is a method based on the differences between the pre-and post-event images, and in which a combination of bands is set for these differences to become visually remarkable. These differences are defined by the change in the intensity value for the same pixel on different dates. The result is a multitemporal image to which a band is assigned to each of the primary colors to form an image of RGB composition [38].
The other methodology used was the calibration threshold technique, also called binarization. It is a simple and quick process that differentiates flooded areas from those that are not by generating histograms. The use of this methodology is not recommended for fast flash flooding, where mapping is urgently needed, and the presence of noise causes some uncertainty [59]. Knowing that the backscatter values in permanent waters and in flooded areas are generally the most negative and differ markedly from the following radiometric values caused by other physical changes, such as, for example, the moisture content of the soil, it is possible to establish any limit [40]. It is necessary to emphasize that an equitable distribution of water and land is necessary so that the histogram has two clear distributions to more precisely limit the threshold. Through the characteristic polarization histogram, we obtained the number of pixels in the image for certain values of intensity or backscatter. In this way, the threshold could be set manually between flooded areas and those that were not. Later, using a mathematical equation of bands, a new bi-colour image separating both zones was achieved [54].

RGB Composition Results
The combination of bands used for the application of the method was the image before the event for the R-band and the image after the event for the G-and B-band. Different results were obtained for the two polarizations, VV and VH, as can be seen in Figure 6  It should be noted that right on the riverbank, the presence of massive riparian vegetation and vertical growth generates a double rebound dispersion or a volume dispersion (Figure 7). The vegetation structure and submerged status are conditioning factors of different scattering mechanisms that can happen, and therefore a range of backscatter values [60]. This, combined with the fact that the sensor used works in the C-band, with a penetration range in bare soil or vegetation that has a maximum of 5 cm, results in the presence of water not being shown even though the area was flooded. These areas are seen in white or slightly pink tones. For these areas, the use of an L-band sensor (23.5 cm) would provide greater penetration because the wavelength is longer than the size of the leaves within the tree mass [61]. In fully submerged locations, scattering dampening occurs as in an open flood, while scattering enhancement is observed in partially or unsubmerged locations because of the dihedral scattering dominates. It should be noted that right on the riverbank, the presence of massive riparian vegetation and vertical growth generates a double rebound dispersion or a volume dispersion (Figure 7). The vegetation structure and submerged status are conditioning factors of different scattering mechanisms that can happen, and therefore a range of backscatter values [60]. This, combined with the fact that the sensor used works in the C-band, with a penetration range in bare soil or vegetation that has a maximum of 5 cm, results in the presence of water not being shown even though the area was flooded. These areas are seen in white or slightly pink tones. For these areas, the use of an Lband sensor (23.5 cm) would provide greater penetration because the wavelength is longer than the size of the leaves within the tree mass [61]. In fully submerged locations, scattering dampening occurs as in an open flood, while scattering enhancement is observed in partially or unsubmerged locations because of the dihedral scattering dominates. A correct choice of polarization optimizes the discrimination of flooded areas [33]. The results obtained in our polarization configurations and the contributions of [41] and [62] in the comparative studies of polarizations to monitor flood areas reaffirm that the VH polarization is more suitable for delimiting flooded areas. It generates well-defined and correctly defined surfaces, results that VV polarization cannot offer. VV polarization is clearly influenced by the roughness and heterogeneity of the terrain [33,63]. We can see this fact in the floodplains, where the roughness of the soil of the crops fields becomes more evident in the VV polarization.

Calibration Threshold Technique Results
Before the application of this methodology for the extraction of several points from the radar images, specifically in zone 1 with VV and VH polarization, a comparison of how backscatter intensity values are modified in areas that have been flooded was considered ( Figure 8). While the magnitude of intensity for the VV polarization, for both moments, is very variable and has a wide spectrum of values, the VH polarization is more defined within a certain range (Figure 9). The areas adjacent to the course of the river, mainly dedicated to cultivation, are characterized by partially rough surfaces and moderate vegetation. These move in values between −17 and −19 dB for the VH polarization, and between −11 and −15 dB for the VV polarization, with specific values that are out of this range. These same areas, subsequently flooded, reflect values between −21 and −24 dB approximately for VH polarization, and for VV, a wider range between −15 and −21 dB. The smooth surface generated by the sheet of water causes the backscatter intensity to decrease towards even more negative values. A correct choice of polarization optimizes the discrimination of flooded areas [33]. The results obtained in our polarization configurations and the contributions of [41,62] in the comparative studies of polarizations to monitor flood areas reaffirm that the VH polarization is more suitable for delimiting flooded areas. It generates well-defined and correctly defined surfaces, results that VV polarization cannot offer. VV polarization is clearly influenced by the roughness and heterogeneity of the terrain [33,63]. We can see this fact in the floodplains, where the roughness of the soil of the crops fields becomes more evident in the VV polarization.

Calibration Threshold Technique Results
Before the application of this methodology for the extraction of several points from the radar images, specifically in zone 1 with VV and VH polarization, a comparison of how backscatter intensity values are modified in areas that have been flooded was considered ( Figure 8). While the magnitude of intensity for the VV polarization, for both moments, is very variable and has a wide spectrum of values, the VH polarization is more defined within a certain range (Figure 9). The areas adjacent to the course of the river, mainly dedicated to cultivation, are characterized by partially rough surfaces and moderate vegetation. These move in values between −17 and −19 dB for the VH polarization, and between −11 and −15 dB for the VV polarization, with specific values that are out of this range. These same areas, subsequently flooded, reflect values between −21 and −24 dB approximately for VH polarization, and for VV, a wider range between −15 and −21 dB. The smooth surface generated by the sheet of water causes the backscatter intensity to decrease towards even more negative values. Water 2019, 11, x FOR PEER REVIEW 13 of 25     Through the graphics obtained by representation of characteristic points, we obtained an idea of the behavior of the radar on different surfaces. The calibration threshold methodology separated the dry zones from areas with the presence of a water sheet through the interpretation of histograms. In Figure 10, the relationship between polarizations and characteristic histograms for each of them is observed for the zone 1: Novillas-Pradilla de Ebro. The histogram of the VV polarization shows a very large curve representative of the intensity values ranging between −1.0 and −22 dB, a very wide spectrum characteristic of areas that range between very rough and moderately rough. While there is no clear projection of low backscatter values, the histogram of the VH polarization itself shows two clear populations where the specific intensity values of the image oscillate. This was chosen for further analysis. On the one hand, there is the curve that provides a greater number of pixels and that represents the areas without water, and on the other hand, a curve of smaller size and with smaller intensity values that represents the areas with water is shown. It is between both areas that the calibration limit can be set. Analyzing the histograms in greater depth, we observe that the cross polarization provides a wider range of backscatter values on surfaces with vegetation than the simple polarization, VV, which results in the latter omitting flooded areas [64]. Through the graphics obtained by representation of characteristic points, we obtained an idea of the behavior of the radar on different surfaces. The calibration threshold methodology separated the dry zones from areas with the presence of a water sheet through the interpretation of histograms. In Figure 10, the relationship between polarizations and characteristic histograms for each of them is observed for the zone 1: Novillas-Pradilla de Ebro. The histogram of the VV polarization shows a very large curve representative of the intensity values ranging between −1.0 and −22 dB, a very wide spectrum characteristic of areas that range between very rough and moderately rough. While there is no clear projection of low backscatter values, the histogram of the VH polarization itself shows two clear populations where the specific intensity values of the image oscillate. This was chosen for further analysis. On the one hand, there is the curve that provides a greater number of pixels and that represents the areas without water, and on the other hand, a curve of smaller size and with smaller intensity values that represents the areas with water is shown. It is between both areas that the calibration limit can be set. Analyzing the histograms in greater depth, we observe that the cross polarization provides a wider range of backscatter values on surfaces with vegetation than the simple polarization, VV, which results in the latter omitting flooded areas [64].  To further specify the boundary point between the areas with water and without water, a water mask was generated on the VH polarization image. This layer was created by randomly capturing some flooded areas within zone 1 and subsequently generating a histogram. In this way, intensity values were obtained in which only areas with water or flooded areas are shown, independent of the frequency or number of pixels taken. It should be noted that this layer serves only to confirm and verify the suitability of the threshold taken with the total histogram of the zone. The comparison of the histogram of the water mask obtained and the polarization VH histogram is shown in Figure 11. A significant spatial linear correlation between both histograms is shown, something that does not happen with VV polarization [56]. After studying the results, we conclude that the most representative values of the presence of the water range mostly between −21 and −24 dB, so −21 dB was considered. The threshold taken was therefore set to −21 dB. To further specify the boundary point between the areas with water and without water, a water mask was generated on the VH polarization image. This layer was created by randomly capturing some flooded areas within zone 1 and subsequently generating a histogram. In this way, intensity values were obtained in which only areas with water or flooded areas are shown, independent of the frequency or number of pixels taken. It should be noted that this layer serves only to confirm and verify the suitability of the threshold taken with the total histogram of the zone. The comparison of the histogram of the water mask obtained and the polarization VH histogram is shown in Figure 11. A significant spatial linear correlation between both histograms is shown, something that does not happen with VV polarization [56]. After studying the results, we conclude that the most representative values of the presence of the water range mostly between −21 and −24 dB, so −21 dB was considered. The threshold taken was therefore set to −21 dB. With the Raster-Band Maths tool of the SNAP program, a binary layer was generated in which, on the one hand, below −21 dB values are represented, representative of the water and flooded areas, and on the other, those above it that would show the dry zones. Figure 12 shows the areas with the presence of water that are observed in part of the study area for a radar image in which no filter was used and another for which the process of Lee 7 × 7 filtering was used. The unfiltered image has a large number of random pixels distributed by the image that give "false positives". All of them are points outside the flood area and the course of the river that have backscatter intensity values below −21 dB. On the other hand, the use of a filter minimizes this effect, favoring less overrepresentation errors. Figure 12. Representation of a specific part of zone 3: Zaragoza-El Burgo de Ebro (the same represented in Figure 5) of the areas that were captured as water in the unfiltered radar image and another with the Lee 7 × 7 filter. With the Raster-Band Maths tool of the SNAP program, a binary layer was generated in which, on the one hand, below −21 dB values are represented, representative of the water and flooded areas, and on the other, those above it that would show the dry zones. Figure 12 shows the areas with the presence of water that are observed in part of the study area for a radar image in which no filter was used and another for which the process of Lee 7 × 7 filtering was used. The unfiltered image has a large number of random pixels distributed by the image that give "false positives". All of them are points outside the flood area and the course of the river that have backscatter intensity values below −21 dB. On the other hand, the use of a filter minimizes this effect, favoring less overrepresentation errors. To further specify the boundary point between the areas with water and without water, a water mask was generated on the VH polarization image. This layer was created by randomly capturing some flooded areas within zone 1 and subsequently generating a histogram. In this way, intensity values were obtained in which only areas with water or flooded areas are shown, independent of the frequency or number of pixels taken. It should be noted that this layer serves only to confirm and verify the suitability of the threshold taken with the total histogram of the zone. The comparison of the histogram of the water mask obtained and the polarization VH histogram is shown in Figure 11. A significant spatial linear correlation between both histograms is shown, something that does not happen with VV polarization [56]. After studying the results, we conclude that the most representative values of the presence of the water range mostly between −21 and −24 dB, so −21 dB was considered. The threshold taken was therefore set to −21 dB. With the Raster-Band Maths tool of the SNAP program, a binary layer was generated in which, on the one hand, below −21 dB values are represented, representative of the water and flooded areas, and on the other, those above it that would show the dry zones. Figure 12 shows the areas with the presence of water that are observed in part of the study area for a radar image in which no filter was used and another for which the process of Lee 7 × 7 filtering was used. The unfiltered image has a large number of random pixels distributed by the image that give "false positives". All of them are points outside the flood area and the course of the river that have backscatter intensity values below −21 dB. On the other hand, the use of a filter minimizes this effect, favoring less overrepresentation errors. Figure 12. Representation of a specific part of zone 3: Zaragoza-El Burgo de Ebro (the same represented in Figure 5) of the areas that were captured as water in the unfiltered radar image and another with the Lee 7 × 7 filter.

Figure 12.
Representation of a specific part of zone 3: Zaragoza-El Burgo de Ebro (the same represented in Figure 5) of the areas that were captured as water in the unfiltered radar image and another with the Lee 7 × 7 filter.
The indicative layers of water presence, for the same specific area, were also generated for the other filters and different window sizes that were considered in the study (Refined Lee, Lee Sigma, and Gamma Map) and through which numerical data of the surfaces that show the presence of a water sheet were extracted (Table 4). It is quite significant that the use of one filter or another provides notable variations in the areas represented as water. Obviously, the unfiltered image is the one that generates more surface compared to any of the others that were filtered. It is quite significant that the use of one filter or another provides notable variations in the areas represented as water. Obviously, the unfiltered image is the one that generates more surface compared to any of the others that were filtered. In comparison, the unfiltered image could provide even more than twice the area flooded than, for example, the image filtered with Lee 7 × 7. Therefore, the application of filtering is necessary not only for speckle reduction but to avoid errors when making quantitative calculations with images. The choice of the Lee 7 × 7 filter, which generates the least area, minimizes the presence of individual pixels outside the flood area and thus prevents overrepresentation in these areas.
The choice of the Lee 7 × 7 filter was verified as being the most appropriate for the results of the present study. The process represented on the PNOA results in the map of zone 1 is shown in Figure 13. It should be taken into account that factors such as the characteristics of the terrain or the angle of incidence, can produce areas of shadow (see this effect in Figure 13a in the upper right where there is a mountainous area with higher topography). These have backscatter values similar to those with surfaces with the presence of water, causing overrepresentation of flooded areas [64]. Therefore, in order to avoid delimitation errors, the threshold technique could be combined with photointerpretation to add areas not identified by default or eliminate areas identified in excess, thus improving the quality of the results obtained [47]. It should also be considered that this technique does not discriminate between bodies of surface water (lakes, lagoons, reservoirs, wetlands) flooded areas. In order to eliminate this overrepresentation, a layer of only permanent areas was generated. This was created from the RADAR image of 6 April 2018, in which the avenue period had not yet begun. By subtracting the flood layer on 13 April 2018 and the one generated on day 6, with the Band Maths tool, we obtained a display of the temporary flood areas that are represented in Figure 13b Following the same steps for the other two zones gave the total flood map for the calibration threshold technique, as represented in Figure 14.  Following the same steps for the other two zones gave the total flood map for the calibration threshold technique, as represented in Figure 14. Following the same steps for the other two zones gave the total flood map for the calibration threshold technique, as represented in Figure 14.  This technique applied to dual polarization images, VH in our case, gives good results in the extraction of water bodies, although these are small, but it has a number of limitations. These include, among other things, background noise. The calibration threshold is dependent on the characteristics of the surface to be captured and also on the specific parameters of the capture [65]. This implies that the threshold is not always fixed and cannot be generalized; it will have to be calculated in each study area.

Photointerpretation with Orthophoto Results
By analyzing the historical data of flows and levels in 2018, we can identify when the maximum annual flows of the Ebro were reached in the area of study. Specifically, they were reached in Novillas on 14 and 15 April at the Pina dam located downstream of the town of El Burgo de Ebro. In the villages between these two points, the maximum flow of the flood occurs between these two dates, migrating according to the direction of the river flow. In view of the data, it can be interpreted that the maximum flood day for the Ribera Alta was 14 April and that a comparison is possible between the RADAR image of 13 April and the orthophotography of the Ribera Alta. A comparison to determine both the flood areas was calculated through the result of the calibration threshold methodology and through photo interpretation of the flooded areas, and also calculation of the flooded surface for a specific day. Therefore, the areas in which the presence of water was perceived as flooding the fields were digitized, generating a vector layer on the orthophotography of the Ribera Alta. At the same time, the Ebro river was also digitized using the PNOA as a base map. For that purpose, the ArcGIS version 10.5 program was used to create the shape layers of the flood area and the Ebro river. In this way, the permanent river channel areas were discriminated from the temporarily inundated areas.
The flood areas of the RADAR image and the orthophotography are combined in Figure 15. In this way, common flood areas were obtained. This technique applied to dual polarization images, VH in our case, gives good results in the extraction of water bodies, although these are small, but it has a number of limitations. These include, among other things, background noise. The calibration threshold is dependent on the characteristics of the surface to be captured and also on the specific parameters of the capture [65]. This implies that the threshold is not always fixed and cannot be generalized; it will have to be calculated in each study area.

Photointerpretation with Orthophoto Results
By analyzing the historical data of flows and levels in 2018, we can identify when the maximum annual flows of the Ebro were reached in the area of study. Specifically, they were reached in Novillas on 14 and 15 April at the Pina dam located downstream of the town of El Burgo de Ebro. In the villages between these two points, the maximum flow of the flood occurs between these two dates, migrating according to the direction of the river flow. In view of the data, it can be interpreted that the maximum flood day for the Ribera Alta was 14 April and that a comparison is possible between the RADAR image of 13 April and the orthophotography of the Ribera Alta. A comparison to determine both the flood areas was calculated through the result of the calibration threshold methodology and through photo interpretation of the flooded areas, and also calculation of the flooded surface for a specific day. Therefore, the areas in which the presence of water was perceived as flooding the fields were digitized, generating a vector layer on the orthophotography of the Ribera Alta. At the same time, the Ebro river was also digitized using the PNOA as a base map. For that purpose, the ArcGIS version 10.5 program was used to create the shape layers of the flood area and the Ebro river. In this way, the permanent river channel areas were discriminated from the temporarily inundated areas.
The flood areas of the RADAR image and the orthophotography are combined in Figure 15. In this way, common flood areas were obtained. To establish the subsequent comparison, both layers were evaluated to calculate the flooded area overlapping the extent of flood areas mapped from both data sources (Table 5). It should be noted that in the RADAR image, the maximum flood areas are located in the upper part of that section of the river, and that in the orthophotography, the maximum flood is in the lower part of the same study area. Considering flood mapping obtained from Sentinel-1 on 13 April 2018, and that flood mapping obtained from ortophotography one day after on 14 April 2018, the fast evolution of the flood areas is clear.
The variability in the value of the flood area is mainly due to the fact that the quality of the RADAR image, which needed numerous treatments, was reduced. This generates a loss of resolution quality that, together with the fact that there were intensity values that were not perceived as flooded areas (tree areas close to the riverbed, mainly), causes a reduction in the calculated area.
In October 2007, the European Directive 2007/60/CE was approved. This directive highlights the need to create flood mapping to delineate areas with greater exposure to these events and to generate hazard and flood risk maps. In accordance with the European Directive 2007/60/CE, Spain's water governance system has made it possible to guarantee security and flood management.
The Flood Risk Management Plans are based on the flood hazard maps (calculation of the flood zone) of the SNCZI for the 10, 50, 100, and 500 year return periods. SNCZI is the official public source for flood hazard information produced by MITECO [66].
The tools used by Spain's government for the creation of the layers showing the flood return periods take into account the following: Hydrological studies that define the flows associated with the return periods considered in each moment; geomorphological studies, including a field cabinet analysis, LIDAR, or orthophotography images; historical studies; and a combination of the previous two and hydraulic studies, such as the generation of two-dimensional models [67]. However, they do not include SAR images as available base information to develop or validate the flood hazard maps.
In Figure 16, the result of the flooding map obtained with the RADAR image is compared with the layers with different return periods (10, 50, 100, 500 years) for a selected zone of the study area of SNCZI. On one hand, all flooded areas derived from Sentinel-1 SAR images are included inside the boundary for flood risk areas to 50, 100, and 500 years return period of SNCZI (Figure 16c-e. While, on the other hand, there are flooded areas that are outside the limits of the 10-year flood risk areas (Figure 16b). Therefore, we conclude that the results of flood area mapping derived from Sentinel-1 images are consistent with those of SNCZI, and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.
Flood maps obtained from Sentinel-1 SAR images complement and validate the information obtained by other means and help to understand the spatial and temporal evolution of the flood. Our results advise using flood maps derived from Sentinel-1 SAR images for the calibration and evaluation of hydrological models and as a data source that can complement and be compared with analysis obtained from other methodologies.

Conclusions
In this study, a functional methodology was proposed for flood monitoring at a necessary spatial resolution derived from SAR images. Specifically, it aimed to highlight the potential use of the Sentinel-1 SAR data sets of the European Earth Observation mission for flood monitoring occurring in the middle course of the Ebro River (Spain) in April 2018.
In SAR images, continental waters have strong contrast in the backscatter values due to their low or null roughness in the absence of waves, behaving like a specular surface that reflects the radar return signal in a direction opposed to the sensor position. However, they need a correction and filtering process (orbital parameters, radiometric, noise reduction speckle, and geometric geometric). Evaluating the different Sentinel-1 parameters, our analysis showed that the best results were obtained using VH polarization configuration. Concerning the need to apply numerous filters to reduce speckle, which also reduces the resolution quality of the images and has an effect on the final flood mapping, Lee filtering with a 7 × 7 window was the most appropriate option and it was proven that it favors the elimination of "false positives".
Two methods were applied to detect and delimitate non-permanent water surfaces from Sentinel-1 SAR images. Method 1 RGB composition is based on differences between the pre-and post-event images, in which a combination of bands is set for identifying these differences as visually remarkable. The calibration threshold technique or binarization is based on a simple and quick process that differentiates flooded areas from those that are not from the characteristic polarization histogram using backscatter values in permanent waters and in flooded areas. The advantages of using the RGB composition methodology is that it guarantees clear differentiation between the areas of permanent flooding and the temporarily flooded ones, while achieving distinction through the technique of calibration thresholds requires other processes. Another advantage of the RGB methodology is that it detects the presence of massive riparian vegetation and vertical growth that can hide the presence of water, and it is essential to get a complete map of the flooded areas. In the calibration threshold technique, the most representative values of the presence of water were mainly between −21 and −24 dB; therefore, a central value of −21 dB was considered.
When comparing our flood maps with the flood areas digitalized from vertical aerial photographs planned by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were consistent, even considering that the dates of both sources differed by a day. This confirmed and validated the use of Sentinel-1 SAR images to obtain flood maps with the required accuracy.
However, our flood maps derived from Sentinel-1 SAR images were consistent with flood risk areas provided by the SNCZI, and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide can be appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.
Finally, the development of methodologies that allow the synergy of different SAR missions (Sentinel-1, PAZ, Radarsat, TerraSAR-X, TanDEM-X, etc.) and multispectral optical systems will increase both the precision and the temporal resolution of flood monitoring, and should also be considered as powerful tools in the management and mitigation of the risks and defense against the adverse effects associated with floods.