# Detection of Surface Water and Floods with Multispectral Satellites
## 3 Multispectral Indices for Water Segmentation
### Classification According to Land Cover  
Multispectral remote sensing-derived indices allow flooded areas and water bodies to be quickly and effectively recognized. However, their performances can vary according to the land cover (i.e., crops, forests, and artificial surfaces), which influences the ability to discriminate between water and other features. To achieve a better understanding of multispectral applications and identify the spectral index that is best suited for a specific land cover setting, we qualitatively analyzed spectral index performances by classifying them according to different land cover types. In particular, the classification was carried out using the selected literature on flooded area mapping, by reviewing case studies and their land cover context, while land surface water detection studies clearly included only one class, i.e., water bodies. In addition, the analyses were carried out on the two spectral indices that showed the best performances in the previous assessment, i.e., the MNDWI and NDWI.  
Figure 4: Results of the performances of spectral indices in detecting land surface water, expressed in terms of overall accuracy (OA). The blue line represents the median value of the OA of each index, while red crosses represent outliers.  
Most of the flood studies included regions in which agricultural areas constitute the main land cover type (e.g., [7; 30; 32; 113]). Other studies were focused on regions mainly covered by forests (e.g., [94; 111; 114]), while only a few included urban areas or artificial surfaces and wetlands (e.g., [6; 115]). Finally, some studies were carried out on the watershed scale (e.g., [8; 29; 116]); the performance metrics, thus, referred to the entire study area, and it was not possible to discriminate among land covers. For this reason, we considered three main classes to carry out a classification of the performances according to the land cover settings, namely, crops, forests, and mixed land. The latter indicate heterogeneous areas that include the contemporary presence of the first two classes, wetlands, urban areas, and/or artificial surfaces. Figure 5 shows the results of the accuracy evaluation of the MNDWI and NDWI for the three selected land cover categories. First, as highlighted above, the MNDWI showed better performances than the NDWI, both in terms of median values and data variation (left panel). Moreover, the MNDWI always showed the best performance in all three land cover categories (upper-right panel) compared with the NDWI in the same categories (lower-right panel). In terms of OA, the median values for the MNDWI were always above 93% for both crops and mixed lands (94.30% and 93.36%, respectively) and above 90% for forest areas (91.40%), while for the NDWI, values never exceeded 90%, with the highest value being observed in forests (88.70%) and the lowest in mixed land (85.98%). For croplands, the NDWI had an OA median value equal to 87%. As regards data variation, MNDWI values showed the lowest spread, while higher variation was observed for NDWI values, with the highest spread of mixed land data.  
For comparison between MNDWI and NDWI performances in flooded area delineation, in Figure 6, the overall accuracy values of the same indices for land surface water detection are reported. As highlighted above, these two indices performed similarly in delineating water bodies; however, some outliers could be detected, mainly due to spatial resolution issues with respect to the water body surface area, turbidity of the water, mixed pixels, or shadows.  
Figure 5: Results of the performances of the MNDWI and NDWI in detecting flooded areas in different land cover settings, expressed in terms of overall accuracy (OA). The blue line represents the median value of the general OA values of MNDWI and NDWI (**left panel**) and of the OA values of the MNDWI and NDWI in each land cover category (**right panels**).