# Detection of Surface Water and Floods with Multispectral Satellites
## 3 Multispectral Indices for Water Segmentation
### Investigation of Spectral Index Performances
(C\({}_{\text{W}}\) and C\({}_{\text{NW}}\) represent the total number of pixels respectively corresponding to water and non-water classes in the classified map, while R\({}_{\text{W}}\) and R\({}_{\text{NW}}\) represent the total number of pixels respectively corresponding to water and non-water classes in the reference map. T is the total number of pixels in the image. )was slightly lower (OA = 92% on average). [PERSON] et al. [29] evaluated the performances of six spectral indices, including the NDWI, MNDWI, AWEI\({}_{\text{sh}}\), and AWEI\({}_{\text{nsh}}\), derived from three different sensors, i.e., Landsat 7-TM+, Landsat 8-OLI, and Sentinel-2 MSI, for water body mapping in Poyang Lake Basin, China. The authors showed the superiority of Landsat 8 and Sentinel-2 over Landsat 7 data and the higher performance of the NDWI in the selected study region (OA values equal to 95.7% and 95.6% for Landsat 8 and Sentinel-2, respectively). [PERSON] et al. [5] applied the spectral index segmentation method using Landsat imagery to map small lakes (from 1 to 30 ha) in Tunisia. The selected indices for validation purposes using Landsat 8 scenes of six different lakes included the MNDWI, NDWI, NDMI, and NDVI. The MNDWI had higher performances in four out of six cases (OA values above 89%), while in the other two sites, the NDWI and NDVI performed better (OA values equal to 80% and 88.1%, respectively). To identify the best-performing spectral indices, we carefully analyzed the selected literature. No distinction was made among different satellite sensors, while accuracy values were evaluated separately for surface water detection and flooding extent delineation. The selected spectral indices for the performance assessment were the MNDWI, NDWI, NDMI, NDVI, AWEI\({}_{\text{nsh}}\), AWEI\({}_{\text{sh}}\), WRI, and MNDWI\({}_{7}\). We summarized the performances of each index with the boxplots reported in Figures 3 and 4. As regards flood mapping studies, the most used spectral indices were the MNDWI, NDWI, and NDMI (39.4%, 30.3%, and 9.1% of use among flood studies, respectively). Only two studies were found that employed the WRI and NDVI (12.2%) and one study that used the AWEI\({}_{\text{nsh}}\), AWEI\({}_{\text{sh}}\), and MNDWI\({}_{7}\) (9%). Figure 3 illustrates the performances of these indices expressed in terms of OA. Among the MNDWI, NDWI, and NDMI, the former was shown to be the best index both in terms of OA median value (93.03%), represented by the horizontal blue line in the box, and the spread of the data. The NDMI had a median value very close to that of the MNDWI (93%); however, the data were more spread out. The same occurred for values of the NDWI, whose median was equal to 87.85%. Figure 3: Results of the performances of spectral indices in detecting flooded areas, 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. Regarding surface water detection, the MNDWI and NDWI were the most common indices (27.8% and 26.4% of use among open water studies, respectively), followed by the AWEInsh (15.3%), NDVI (11.1%), AWEIsh (9.7%), WRI (4.2%), NDMI, and MNDWI7 (5.6%). Figure 4 illustrates the performances of these indices expressed in terms of OA. In this case, the MNDWI had an OA median value of 95.37% (horizontal blue line in the figure) and a relatively small spread of the data. However, some outliers were observed. The NDWI and AWEInsh had similar performances to those of the MNDWI, in terms of median value (94.41% and 94.80%, respectively), data spreading, and outlier values. The NDMI showed the lowest median value among all selected spectral indices (median OA value of 88.80%). Although the highest OA median value was observed for the WRI, only five studies were available, which may not be sufficient to satisfactory interpret its performance. It is worthy to underline that the analyses presented herein did not consider the potential impact of the specific processing workflow adopted by each author.