# Detection of Surface Water and Floods with Multispectral Satellites
## 2 Multispectral Satellite Remote Sensing for Flooded Area and Wetland
### Flood Mapping Approaches Using Optical Remote Sensing
Such information assumes the form of large training datasets that must adequately describe the classification problem and contain representative class samples [20; 69; 70]. An alternative is represented by unsupervised approaches, mainly based on Iterative Self-Organizing Data Analysis (ISODATA) clustering [81]. For example, [PERSON] et al. [46] identified in mandated areas in Macquarie Marshes in central-eastern Australia, using Landsat Multispectral Scanner System (MSS), TM, and 7-ETM+ images over 28 years and applying ISODATA, while [PERSON] et al. [82] used the same classification algorithm to extract flood extent from Landsat 5-TM data and estimate the relationship between the flood discharge and the elevation extracted using a Digital Elevation Model (DEM) at the flood extent boundaries. Since unsupervised methods do not require training samples, they are especially adopted when there is scarce knowledge about the classification problem [69] and can easily be transferred to different contexts [20]. In many studies, remote sensing data and derived flood extents were integrated or used in conjunction with ancillary data to help to avoid flooded area underestimation and misclassifications, especially caused by vegetation and forest cover [62]. Auxiliary information included DEMs and Light Detection and Ranging (LiDAR) products (e.g., [62; 65; 83; 84; 85]), or derived geomorphic indices, such as the Height Above the Nearest Drainage index (HAND [86]; also defined as the elevation to the nearest stream, H) and the Geomorphic Flood Index (GFI [87; 88]). [PERSON] et al. [89] carried out a comparative analysis of geomorphic descriptors (i.e., H and the GFI) and satellite-based spectral indices derived from Landsat 8-OLI images for flood-prone area delineation. More recently, [PERSON] et al. [90] implemented an innovative Flood Mapping Algorithm (FMA) on the cloud platform Google Earth Engine (GEE) using the MNDWI for water classification and filtering dark and steep vegetated hilly areas with NDVI and HAND maps, respectively. Finally, from the proposed review, it was interesting to note some emerging trends in the last few years, especially the increasing interest in cloud computing for processing remote sensing products. The already mentioned GEE, in fact, allows data visualization and the analysis of ready-to-use satellite data and geospatial products to be performed at the planetary scale [91], and these can be collected from a vast archive, including Landsat imagery since 1982, and MODIS and Sentinel collections. Since 2016, several authors have developed tools and automated flooded area mapping algorithms on GEE to delineate the extent of the event or generate time-series flood maps [92; 93; 94; 95]. Others just exploited the GEE environment capabilities of managing large amounts of data and offering parallel computations to process satellite data [96; 97]. A summary of the references considered herein that suggest different methods for flood monitoring and the adopted datasets are given in Table 2.