# Detection of Surface Water and Floods with Multispectral Satellites
## 3 Multispectral Indices for Water Segmentation
### Investigation of Spectral Index Performances  
In our investigation, we noticed that descriptive metrics are not always all used simultaneously to assess the performances of multispectral indices, with OA being the most commonly used. Therefore, to have a homogeneous and comparable metric among different studies, a further screening of the literature was necessary to take into consideration those that at least used OA as a performance metric. This was considered as a summary statistic for assessing the ability of the selected spectral indices to detect water pixels. The performance assessment was separately carried out for studies on flood mapping and surface water detection (i.e., water body mapping) that employed either Landsat 5-TM, Landsat 7-ETM+, Landsat 8, Sentinel-2, or MODIS or presented a comparison of more than one dataset. Among flooded area detection studies (42% of total studies), for example, [PERSON] et al. [30] proposed a comparative analysis of several spectral indices to map flooded rice cropping systems using MODIS data. Validation on pure water pixels showed that among 11 selected indices, the best mapping accuracy was achieved by those based on the SWIR and visible bands, particularly the MNDWI (OA = 97%). Similarly, [PERSON] et al. [32] compared different inundation mapping methodologies, including supervised and unsupervised classification techniques, a change detection approach, and two spectral indices, i.e., the MNDWI and NDWI, based on Landsat 8-OLI satellite imagery. Despite the fact that other methods led to better performances, both indices achieved satisfactory results (OA values equal to 77.3% and 77.1% for the MNDWI and NDWI, respectively). [PERSON] et al. [6] assessed the performances of seven water index algorithms, including, among others, the MNDWI, NDWI, NDMI, NDVI, and AWEInsh, derived from Sentinel-2A MSI and Landsat 8-OLI for coastal surface inundation mapping. In this case, indices from both sensors showed good accuracy (OA values above 94%). Using Sentinel-2 MSI data, [PERSON] et al. [111] performed an MNDWI-based segmentation to separate water and land features to ultimately characterize extreme flood impacts on the channel-floodplain morphology and sediment regime of the river system. The classification accuracy achieved 96%. More recently, [PERSON] et al. [94] combined Landsat images with precipitation data and high-resolution satellite imagery in the GEE environment to evaluate channel-floodplain dynamics. The authors used the MNDWI to extract the flooding extent, based on visual interpretation, achieving 93% of accuracy. A higher number of studies that employed multispectral indices were found for surface water body detection (58% of total studies). One of the most significant works is the one proposed by [PERSON] et al. [112], in which Advanced Land Imagery (ALI) data and Landsat 5-TM and ETM+ images were selected to compare land surface water mapping based on the MNDWI, NDWI, and MNDWI? in three different study sites. Despite the aim of the authors being to demonstrate the superiority of ALI data, Landsat imagery succeeded in delineating water features in all three regions. In particular, the indices based on the SWIR bands (i.e., the MNDWI and MNDWI?) showed very high and similar performances (OA values equal to 94.6% and 93.9% on average, respectively), while the accuracy of the NDWI \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference Map**} \\ \hline  & **Class** & _Water_ & _Non-Water_ & **Row Total** \\ \hline \multirow{2}{*}{**Classified Map**} & _Water_ & TP & FP & C\({}_{\text{W}}\) \\  & _Non-water_ & FN & TN & C\({}_{\text{NW}}\) \\ \hline  & **Column total** & R\({}_{\text{W}}\) & R\({}_{\text{NW}}\) & **T** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of a confusion matrix for water pixel classification.