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A novel flood/water extraction index (FWEI) for identifying water and flooded areas using sentinel-2 visible and near-infrared spectral bands

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Abstract

Accurate assessment of surface water from satellite and remote sensing data plays an important role in water and flood management and supporting natural ecosystems and human development. Remote sensing imagery has significantly advanced in water extraction methods, particularly in water index, classification, and sub-pixel analysis. Water-index-based approaches offer notable advantages such as speed and convenience among these methods. The unique characteristics of surface water and flooded areas, including their extensive coverage and dynamic nature, make the water index particularly effective for monitoring large regions. However, the complexity of land surfaces in aquatic environments presents challenges that hinder accurate water extraction. These challenges differ across various factors, such as shadows in urban and mountainous areas, small water bodies, muddy water, and water leakage in unshaded regions. The current study introduces a novel Flood/Water Extraction Index (FWEI) for identifying water and flooded areas to address these challenges. The FWEI utilizes the average ratio of visible and near-infrared bands derived from Sentinel-2 images. The proposed index utilizes images with 10-m and average visible bands and more effectively compensates for errors arising from spectral and spatial changes. Therefore, it demonstrates strong performance by more accurately mapping muddy and clear water within small water bodies and narrow rivers. The performance of the offered FWEI index is compared with other indices, including the Normalized Difference Water Indices (NDWI-G, NDWI-F), Modified NDWI (MNDWI-1, MNDWI-2), and the Automatic Water Extraction Index (AWEInsh) without shadow. While other indices excel in specific scenarios, such as built-up or non-built-up areas, and bare lands versus vegetated areas, the FWEI index demonstrates consistently high accuracy and stability in extracting surface water across diverse backgrounds. The FWEI index achieves an average Overall Accuracy (OA) of 94.26% for water extraction and 93.11% for flood extraction. In comparison, the AWEInsh attains an OA of 90.48% and 90.39%, NDWI-F performs at 86.69% and 86.55%, MNDWI-1 at 77.21% and 75.82%, MNDWI-2 at 76.12% and 75.42%, and NDWI-G at 75.26% and 74.78%, respectively. The integration of visible spectral bands with the near-infrared band proves instrumental in enhancing the accuracy of water derivation in complex and expansive environments.

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Data availability

Upon request, the corresponding author is willing to share the datasets analyzed in this research.

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Funding

This study did not receive public or commercial funding agencies' grants, funds, or other support.

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Authors and Affiliations

  1. Faculty of Geodesy and Geomatics Engineering, K.N Toosi University of Technology, Tehran, Iran

    Hadi Farhadi & Hamid Ebadi

  2. Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Abbas Kiani

  3. Disaster & Emergency Management, School of Administrative Studies, Faculty of Liberal Arts and Professional Studies, York University, Toronto, ON, Canada

    Ali Asgary

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  1. Hadi Farhadi
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  2. Hamid Ebadi
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  3. Abbas Kiani
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  4. Ali Asgary
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Contributions

HF: Introduction, material and method, Programming, visualization, data processing, result and discussion, validation, original draft, analysis, and review & editing. HE, AK and AA: Analysis, review & editing, supervision.

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Correspondence to Abbas Kiani.

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Farhadi, H., Ebadi, H., Kiani, A. et al. A novel flood/water extraction index (FWEI) for identifying water and flooded areas using sentinel-2 visible and near-infrared spectral bands. Stoch Environ Res Risk Assess 38, 1873–1895 (2024). https://doi.org/10.1007/s00477-024-02660-z

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