Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI)

• Published in: Computers & geosciences 2025-12, Vol.194, p.105742, Article 105742
• Main Authors: Farhadi, Hadi, Ebadi, Hamid, Kiani, Abbas, Asgary, Ali
• Format: Article
• Language: English
• Subjects: Automatic water mapping; Flood mapping; Inundated areas detection; Remote sensing; Spectral index
• ISSN: 0098-3004
• DOI: 10.1016/j.cageo.2024.105742

Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods