# Potential and Limitations of Open Satellite Data for Flood Mapping
## 5 Discussion  
The results of our study show that choosing the best satellite data or the most appropriate processing aimed at detecting flooded areas is a complicated process. It is necessary to consider the complex relationships between the flood and the satellite characteristics. In the following, we introduce a more detailed description of the parameters that play a fundamental role:  
* The time factor is the most critical parameter. If a co-flood image is available, the detection and the refinement of the inundated area are more straightforward and require a short time. To know the time of maximum inundation, data from river gauge stations (e.g., for the Ebro, Po, and Tanaro case studies) can be considered. If river gauge station data are not available (e.g., Arahal case study), it is often possible to estimate the time of inundation from ancillary data found on the web such as news, photos, or videos. Medium-low resolution multispectral satellites (MODIS, Proba-V, Sentinel-3), with their daily revisit time, are more likely to acquire co-flood images for many flood events. However, it is less probable to have co-flood SAR images acquired by Sentinel-1, which in 2018 are the only SAR satellites providing free images. The time elapsed from the flood peak also influences the band ratios to use; MNDWI\({}_{\text{var}}\) shows better performance in the short term, whereas NDVI\({}_{\text{var}}\) and VIS\({}_{\text{var}}\) have better performances over the long term because MNDWI is more sensitive to open water; changes in NDVI and VIS are primarily related to vegetation damage or silt deposits in flooded areas. Concerning the pre-flood images used as a benchmark, the one acquired as close as possible to the flood event should be selected to have similar conditions of land use and sun illumination. * The use of a single acquisition date with respect to image differences with pre-flood must be considered in the semi-automatic mapping: In the first case, permanent water bodies outside flooded areas are wrongly classified as flooded. In the second case, the water bodies inside the flooded area are not classified as flooded. This issue can be solved using a mask of the permanent water body. * The sky-condition is another relevant parameter that limits the availability of co-flood images. Indeed, only SAR satellites can acquire images under cloudy conditions. In our cases, the area flooded around Zaragoza was cloud free during the 2015 flooding because the flood peak arrived several days after the meteorological event, whereas cloudy conditions did limit the availability of images for the 2016 Po basin flood. * The spatial features of the flooded area represent a constraint on satellite spatial resolution. Small, inundated areas (e.g., Arahal case study) cannot be mapped by low-resolution satellites. In addition, an area flooded by a small stream has a very short co-flood time interval. * The extent of the affected area. For the main river, it might require more than one image to map the whole river section affected by the flood. In addition, the time of maximum flood changes along the stream and is not the same for tributaries. For instance, the flood waves of the Po and Ebro Rivers transited over several days from upstream to downstream. * Land use and morphology of the flooded area. These factors are important in choosing the satellite image. Inundations occurring on cultivated crops in floodplains can be easily detected by most satellites. In addition, several days after the flood, with multi-spectral data, it is possible to recognize flooded areas easily, particularly if sediments cover crop fields. In urban or densely forested regions, detection of the flooded areas is more complicated. In the arid region, the effect on NDVI can be opposite, with an increase of vegetation index in the flooded area [67]. In many cases, high-resolution data and field surveys remain necessary for a reliable mapping. * With the semi-automatic approach, we used empirical thresholds to detect flooded areas.