# Detection of Surface Water and Floods with Multispectral Satellites
## 4 Discussion
Difficulties in water segmentation can largely be connected to the presence of cloud cover and shadows, but this is not the only limiting factor. In fact, the ability of multispectral indices to reliably identify water features, especially flooded areas, can also be related to the land cover. Considering changes in land and vegetation coverage due to dynamic processes occurring during and after floods, the surface spectral reflectance can be affected. For this reason, we analyzed the overall accuracy values of the spectral indices according to three different land cover classes, i.e., crops, forests, and mixed areas. The results showed that for flood mapping, the MNDWI was found to be the best index in all the land cover settings, outperforming other indices especially in mixed lands, confirming its ability to discriminate mixed pixels. The comparison of the accuracy metrics from different research studies had the general purpose of providing further insight into the sensitivity of multispectral indices in detecting water features in different flooded land cover types. We are aware that such a comparison may suffer from some shortcomings, including the fact that validation methods of satellite-derived flood maps can vary from study to study and be based on different reference data (e.g., retrieved from satellite products themselves or ground-based), thus introducing a further source of error. Despite such limitations, the outcomes represent a potential reference to discriminate between different methods. Our analyses included all the spectral indices used in flood mapping studies; however, good performances for detecting water areas could also be achieved using other vegetation indices, such as the Water-Adjusted Vegetation Index (WAVI). It was introduced by [PERSON] [121] to retrieve information about aquatic vegetation and distinguish it from the terrestrial one. Applications also included the classification of macrophye community types [122] and the detection of vegetation overgrowth processes in reservoirs [123]. Since aquatic vegetation relies on the presence of water, the application of WAVI for flood mapping studies can have some good potential, not yet explored in the literature. The accuracy of water extraction methods, the thresholds for image classification, and the interpretation of results may differ based on the pre-processing chain adopted to solve issues related, for example, to sensor, atmospheric, and geometric noises [2; 105]. Optical imagery, in fact, needs a proper pre-processing stage prior the calculation of water indices, especially if the available products are in the form of raw DN values. A typical workflow to pre-process remote sensing data includes radiometric calibration (to convert DNs to at-sensor spectral radiance, i.e., Top Of Atmosphere - TOA) and atmospheric corrections (to convert at-sensor radiance to at-surface reflectance, i.e., Bottom of Atmosphere - BOA). In particular, radiometrically and atmospherically corrected Landsat images are frequently obtained by applying tools available in ENVI image analysis software, such as the Radiometric Calibration tool and the Fast Line-of-Sight Atmospheric Analysis of Spectral Hyper-cube (FLAASH) module (e.g., [4; 105; 124; 67; 105]), while for Sentinel-2 data water detection approaches are directly applied to level 1C (TOA products, e.g., [125]) or 2A (BOA products, e.g., [111]). It is worth noting that the investigation of the specific pre-processing workflow adopted in each work considered in our analyses was not within the scope of the present manuscript. Nevertheless, the influence of such methods on the water index accuracy values requires more in-depth investigations.