Reducing false alarms in urban flood detection: an enhanced NDWI (ENDWI) with Hybrid Max Fusion on Sentinel-2 Data

Almoadi, Abdulrhman M.

doi:10.5194/eo-1-43-2026

Articles | Volume 1, issue 1

https://doi.org/10.5194/eo-1-43-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/eo-1-43-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 1, issue 1

Research article

|

06 Aug 2026

Research article |

| 06 Aug 2026

Reducing false alarms in urban flood detection: an enhanced NDWI (ENDWI) with Hybrid Max Fusion on Sentinel-2 Data

Abdulrhman M. Almoadi

Cited articles

Ahmadi, P., Valadan Zoej, M. J., Mokhtarzade, M., Kardan, N., Ahmadi, P., and Ghaderpour, E.: TLE-FEDformer: A Frequency-Domain Transformer Framework for Multi-Sensor Multi-Temporal Flood Inundation Mapping, Remote Sens., 18, 895, https://doi.org/10.3390/rs18060895, 2026.

Albertini, C., Gioia, A., Iacobellis, V., and Manfreda, S.: Detection of Surface Water and Floods with Multispectral Satellites, Remote Sens., 14, 6005, https://doi.org/10.3390/rs14236005, 2022.

Almoadi, A.: aalmoadi/endwi: Z-Split Normalization v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.20602710, 2026.

Amitrano, D., Di Martino, G., Di Simone, A., and Imperatore, P.: Flood Detection with SAR: A Review of Techniques and Datasets, Remote Sens., 16, 656, https://doi.org/10.3390/rs16040656, 2024.

Bersabe, J. T. and Jun, B.-W.: The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data, ISPRS Int. J. Geo-Inf., 14, 57, https://doi.org/10.3390/ijgi14020057, 2025.

More articles (28)

Short summary

Urban floods are difficult to detect accurately in satellite imagery because non-water surfaces often resemble floodwater, causing false alarms. This study introduces an enhanced normalized difference water index (ENDWI), derived from the normalized difference water index (NDWI), combined with a second hybrid index to reduce these false alarms using freely available Sentinel-2 imagery. False alarms were reduced from 38 % to under 3 %, offering a more reliable, low-cost approach to urban flood mapping.