# 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  
Satellite observations from optical sensors have long been used for river monitoring and to assess inundation dynamics in the pre- and post-flood emergency. In detail, flooded areas and wetland inundations have been mapped using Landsat [41; 42; 43; 44; 45; 46; 47; 48], MODIS [49; 50; 51; 52], and Sentinel-2 imagery [53; 54; 55; 56] in different contexts, including urban [57; 58] and coastal environments [59; 60; 61; 62]. Satellite-derived flood maps also represent support for emergency responses and the evaluation of flood severity and costs. Several studies assessed post-flood damages to infrastructures, built-up, and agricultural areas [63; 64; 65; 66; 67; 68], as well as the estimation of flood impacts on natural environments and floodplain ecosystems (e.g., [61; 62]). Different methodologies were applied to detect flooded areas. A comprehensive review of optical images classification methods was carried out by [PERSON] et al. [69] and can also be found in [PERSON] et al. [70] and [PERSON] et al. [71]. Most of the studies adopted segmentation approaches to discriminate between water and non-water pixels. They included density slicing to a single band by visually inspecting the grayscale histogram of the satellite images or thresholding the different band ratios or compositions that form the spectral indices. [PERSON] and [PERSON] [72] analyzed the vegetation response to floodplain inundation using Landsat TM images. The authors applied a ratio of Band 1 to Band 7 to map deep open water, while the change detection technique applied to Band 5 for processing preflood images and those acquired during the flood event was used to identify shallow water. NDVI maps were then used to study the relation between vegetation growth and flood frequency, from which it emerged that where vegetation was more vigorous, occurring where high NDVI values could be detected, flooding was registered less frequently. [PERSON] and [PERSON] [73] applied density slicing to Landsat TM infrared Band 5 to classify water and non-water regions and relate wetland inundation with river flood peak discharge. By observing Digital Number (DN) values in the red band of MODIS images, [PERSON] et al. [48] identified threshold values for classifying inundated and non-inundated areas to ultimately define flooding frequency and duration from 2001 to 2012 in Okavango Delta (Botswana, Africa). Similarly, image thresholding was applied by [PERSON] et al. [55] to multi-temporal Sentinel-2 imagery for large-scale wetland mapping  
Figure 1: Number of research studies on flooded area and wetland inundation mapping from 2002 to 2021 using Landsat, MODIS, or Sentinel-2 imagery retrieved from the Scopus academic database. mainly based on spectral indices. Thresholding represents an easily implemented technique for flood mapping based on the selection of a single appropriate threshold value from a bimodal intensity histogram that partitions the image into two classes, i.e., water and non-water. One of the most common thresholding approaches is [PERSON]'s method, aimed at maximizing the inter-class or, similarly, minimizing the intra-class variance [74]. Difficulties arise because in most of the cases, the histograms are not bimodal, but several classes are present in the flooded scene, and disturbance factors, such as dense vegetation canopies, do not allow one to identify the presence of water beneath them [71]. Supervised classification was also found to be one of the most widely used approaches for flood extent delineation and/or land cover/land use classification to assist with water detection. This method includes maximum likelihood (e.g., [66; 75]), random forest (e.g., [76; 42; 77; 78]), and support vector machine (e.g., [79; 80]). Despite the large use of supervised classifiers, their use requires a priori knowledge of the classes to be identified.