# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab  
[PERSON]  
1 Institut et Observatoire de Geophysique d'Antananarivo, Universite d'Antananarivo, Antananarivo 101, Madagascar; [EMAIL_ADDRESS] (S.R. ); [EMAIL_ADDRESS] (S.R.) [PERSON]  
2 UMR 228 Egspace Dev, Universite de La Reunion, 97400 Saint-Denis, Reunion Island, France; [EMAIL_ADDRESS] (C.R. ); [EMAIL_ADDRESS] (G.P)  
[PERSON]  
3 UMR 228 Egspace Dev, IRD, Maison de la Teideetection, 34090 Montpellier, France; [EMAIL_ADDRESS] (T.C. ); cyprinaelaelareadreadread.fr (C.A.) [PERSON]  
3 UMR 228 Egspace Dev, IRD, Maison de la Teideetection, 34090 Montpellier, France; [EMAIL_ADDRESS] (T.C. ); cyprinaelareadreadread.fr (C.A.) [PERSON]  
4 UMR 228 Egspace Dev, IRD, SEAS-OI, 97410 Saint-Pierre, Reunion Island, France; [EMAIL_ADDRESS] (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelareadread.fr (C.R. ); cyprinaelaread.fr (  
also quickly helps identify large-scale flooded areas, aids in disaster relief planning, and contributes to damage quantification in the event of a cyclonic event [7]. Flood map products are generated by a range of international organisations depending on the severity of specific cyclone events. ESA's rapid mapping products are one example [8]. They are usually obtained at the request of local authorities and/or through the International Charter Space and Major Disasters [9]. The FAO also produces flood maps for assessing food security [10]. However, these maps only cover relatively localised areas hit by major cyclones and not all territories affected by flooding. This level of event coverage justifies the need for other larger mapping products at the scale of the affected territories. Because floods are accompanied by dense cloud cover, the utility of optical remote sensing approaches is nevertheless limited. Indeed, optical remote sensing can lead to an underestimation of flood extents due to the lack of timely cloud-free satellite imagery to coincide with cyclone events [11]. Earth imaging systems like Landsat 8 and 9 or Sentinel-2 are therefore strongly constrained. Furthermore, intertropical zones are subject to frequent cloud cover. Radar satellite imagery offers a way around this issue, as the wavelengths in radar imagery are on the order of a centimetre, so the atmosphere is almost transparent to them [12]. A flood is an uncommon body of standing water. Such surface water is detected by radar data because backpropagation radar waves from the water surface are much weaker than from other surfaces [13; 14; 15]. However, radar imaging systems are also sensitive to surface roughness and the dielectric properties of the surface [16]. It is also possible that the radar response from a standing water surface and a smooth soil surface will be similar. Moreover, side-view radar systems can create shadows in imagery. Comparing two radar images acquired before and during or just after a flood happened enables low backscatter features and permanent water bodies to be filtered out [15]. Image comparison supports flood analysis and can be performed using supervised or unsupervised [17; 18] change detection algorithms [19; 20]. It can be performed using pixel-by-pixel simple algebraic operations like a ratio [21] or textural indices [22]. Comparisons can also be operated on previously identified features [23]. The ratio between two images attenuates the speckle effect in radar images [24]. Machine learning is also increasingly used in this field [25]. Phase information from each pixel can also be used to identify changes. Interferometric coherence has been employed to detect floodwaters in urban areas [26]. A digital elevation model (DEM) and water flow properties can improve flood detection by masking and reducing areas to be processed.