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Mapping global floods with 10 years of satellite radar data

Author

Listed:
  • Amit Misra

    (Microsoft AI for Good Research Lab)

  • Kevin White

    (Microsoft AI for Good Research Lab)

  • Simone Fobi Nsutezo

    (Microsoft AI for Good Research Lab)

  • William Straka

    (University of Wisconsin-Madison)

  • Juan Lavista

    (Microsoft AI for Good Research Lab)

Abstract

Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods remain scarce. In this study, we introduce a deep learning flood detection model that leverages the cloud-penetrating capabilities of Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery, enabling consistent flood extent mapping through cloud cover and in both day and night conditions. By applying this model to 10 years of SAR data, we create a unique, longitudinal global flood extent dataset with predictions unaffected by cloud coverage, offering comprehensive and consistent insights into historically flood-prone areas over the past decade. We use our model predictions to identify historically flood-prone areas in Ethiopia and demonstrate real-time disaster response capabilities during the May 2024 floods in Kenya. Additionally, our longitudinal analysis reveals potential increasing trends in global flood extent over time, although further validation is required to explore links to climate change. To maximize impact, we provide public access to both our model predictions and a code repository, empowering researchers and practitioners worldwide to advance flood monitoring and enhance disaster response strategies.

Suggested Citation

  • Amit Misra & Kevin White & Simone Fobi Nsutezo & William Straka & Juan Lavista, 2025. "Mapping global floods with 10 years of satellite radar data," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60973-1
    DOI: 10.1038/s41467-025-60973-1
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    References listed on IDEAS

    as
    1. Jean-François Pekel & Andrew Cottam & Noel Gorelick & Alan S. Belward, 2016. "High-resolution mapping of global surface water and its long-term changes," Nature, Nature, vol. 540(7633), pages 418-422, December.
    2. Brenden Jongman, 2021. "The fraction of the global population at risk of floods is growing," Nature, Nature, vol. 596(7870), pages 37-38, August.
    3. B. Tellman & J. A. Sullivan & C. Kuhn & A. J. Kettner & C. S. Doyle & G. R. Brakenridge & T. A. Erickson & D. A. Slayback, 2021. "Satellite imaging reveals increased proportion of population exposed to floods," Nature, Nature, vol. 596(7870), pages 80-86, August.
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