# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Data Processing, Analysis and Validation
#### 3.3.3 Semi-Automatic Mapping Based on Multispectral Image Processing  
The first presented method is based on the analysis of a single image. The others are based on a comparison of two images (post or co-flood against pre-flood images). The thresholds used to discriminate flooded from not-flooded pixels are empirically based on a visual approach; we set the threshold that best defines a geomorphology- based flood pattern. * **Supervised classification (SC)**. We applied this technique to classify MODIS and Landsat-8 co-flood data. We initially detected the most representative land-cover types (i.e., water-covered area, vegetation, cloud, snow, and urban area/bare soil) in training areas selected over a composite band image on QGIS to create a spectral signature with the available bands. Then, we performed a supervised classification of the images using the SAGA-GIS. Specifically, we tested different classification methods and selected the maximum likelihood and spectral angle methods as the most appropriate for our study. Finally, using a raster query in QGIS, we extracted the \"area covered by water or wetland\" category. In this case, the use of a permanent water body mask also allowed us to separate the flooded area from the permanent rivers and lakes. * **Normalized Difference Vegetation Index variation (NDVIvar)**. We calculated the NDVI variation between the pre- and post-flood conditions (Equation (1)). The aim is to identify areas characterized by a decrease in NDVI values due to vegetation activity decreasing (areacovered by sediments or damaged vegetation) or the presence of water covering the area [75; 76]. We computed NDVI using the NIR and the red band of Sentinel-2 (10 m of spatial resolution), Landsat 8 (30 m of S.R. ), Proba-V (100 m of S.R.) and MODIS data (250 m of S.R. ): \[\mathrm{NDVIvar}=\mathrm{NDVIpost}-\mathrm{NDVIpre}\ \mathrm{where}\ \ \ \mathrm{NNDVI}=\frac{(\mathrm{NIR-Red})}{(\mathrm{NIR+Red})}\] (1) Modified Normalized Difference Water Index variation (\(\mathrm{NDVI_{var}}\)) and Normalized Difference Moisture Index variation (\(\mathrm{NDMI_{var}}\)). These indexes allow the detection of water bodies or wetlands and were widely used to map flooded areas [77]. In the literature, different combinations for calculating \(\mathrm{MNDWI}\) are presented and discussed [78; 79; 80]. * In our study, we used the Red Edge band--Short Wavelength Infrared bands (Equation (2a)) for Sentinel-2 data (20 m of S.R. ), and the Red and SWIR bands for Landsat-8 (30 m of S.R. ), MODIS (500 m of S. R.) and Proba-V data. In a pre-flood situation, these bands better supported noticing changes in soil moisture/water covered areas. The \(\mathrm{NDMI}\)[81] is calculated by exploiting the NIR and SWIR bands and was used for the Arahal area (Equation (3)). \[\mathrm{MNDWIvar}=\mathrm{MNDWIpost}-\mathrm{MNDWIpre}\] (2) where \[\mathrm{MNDWI}=\frac{(\mathrm{RedEdge1}-\mathrm{SWIR})}{(\mathrm{RedEdge1}+ \mathrm{SWIR})}\] (2a) or \[\mathrm{MNDWI}=\frac{(\mathrm{Red}-\mathrm{SWIR})}{(\mathrm{Red}+\mathrm{SWIR})}\] (2b) \[\mathrm{NDMIvar}=\mathrm{NDMIpost}-\mathrm{NDMIpre}\ \mathrm{where}\ \mathrm{MNDWI}=\frac{(\mathrm{NIR}-\mathrm{SWIR})}{(\mathrm{NIR}+\mathrm{ SWIR})}\] (3)
* **Variation of averaged visible bands (\(\mathrm{VIS_{var}}\)). ** This index allows the detection of the reflectance variation induced by the presence of silt deposits on crop fields. The index is based on the averaging of the RGB bands (Equation (4)). We derived this index from the Sentinel-2 data (S.R. 10 m) acquired over the Tanaro and Po areas, allowing noting of the sediment deposits and mapping the flooded areas indirectly. \[\mathrm{VISvar}=\frac{(\mathrm{VISpost}-\mathrm{VISpre})}{(\mathrm{VISpost}+ \mathrm{VISpre})}\ \mathrm{where}\ \mathrm{VIS}=\frac{(\mathrm{R}+\mathrm{G}+\mathrm{B})}{3}\] (4)
* **SAR-Optical combination:** The extraction of the flooded area for the Arahal case study was also obtained by integrating the processed SAR (Sentinel-1) and multispectral (Sentinel-2 and Landsat-8) data.