###### Abstract  
Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu, and Fuzzy Otsu algorithms for the satellite images; the FwDET tool was deployed upon the cloud computing platform (Google Earth Engine) for rapid estimation of flood area/depth using the digital elevation model (DEM) data. Generated surface water maps from the respective techniques were evaluated qualitatively against each other and compared with a reference map produced by the European Union Copernicus Emergency Management Service (CEMS). The DEM-based maps show generally overestimated flood extents. The satellite-based maps show that Edge Otsu and Bmax Otsu methods are more likely to generate consistent results than those from Fuzzy Otsu. While the synthetic-aperture radar (SAR) data are typically favorable over the optical image under undesired weather conditions, maps generated based on SAR data tend to underestimate the flood extent as compared with reference maps. This study also suggests the newly launched Landsat-9 serves as an essential supplement to the rapid delineation of flood extents. a 2023 10 17 10.3390 10010017 10.3390 10010017 10. -flood forecasting. [PERSON] [13] used satellite-borne synthetic-aperture radar imagery as calibration and validation data for a two-dimensional finite element flood flow model. [PERSON] et al. [15] assimilated Advanced SCATerometer (ASCAT) soil moisture products to improve the simulations of a rainfall-runoff model. [PERSON] [17] presented a high-resolution topographic mapping (30 m) of the middle-lower Amazon floodplain using Landsat imagery. Meanwhile, efforts have also been made to improve the algorithms for automated surface water mapping using remote sensing data. These algorithms include (1) thresholding of single bands and spectral indexes [19; 20], (2) knowledge-based water detection from other land cover types based on the spectral properties [21; 22], spectral mixture analysis (SMA) [23; 24], and machine learning methods including both supervised and unsupervised classification schemes [25; 26]. [PERSON] et al. [27] applied a fully automated and scalable algorithm that estimated the subpixel water fraction using Landsat data over large areas. [PERSON] et al. [28] presented an automated surface water extraction algorithm using Sentinel-1 synthetic-aperture radar (SAR) imagery. [PERSON] et al. [29] applied an unsupervised approach based on the automatically selected threshold on the shortwave infrared (SWIR) band to better distinguish between different types of flooded wetlands using Sentinel-2 optical images. [PERSON] et al. [30] developed an automated algorithm to find the image segmentation threshold for operational flood mapping using Sentinel-1 SAR Imagery. [PERSON] et al. [31] created a similar segmentation algorithm that extracts water edges for threshold sampling. [PERSON] et al. [3] utilized a self-supervised machine learning framework for patch-wise urban flood mapping using bitemporal multispectral satellite imagery. [PERSON] et al. [26] evaluated multiple machine learning algorithms such as naive Bayes (NB), support vector machines (SVM), and random forest (RF) to extract surface water in Nepal. [PERSON] et al. [32] presented an SAR-based automatic approach to assess the water surface changes in Saskatchewan, Canada, where the expectation maximization (EM) algorithm [33] was applied to obtain maximum likelihood estimates of the mixture parameters.