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<p>Reliable and up-to-date coastal bathymetry remains scarce for many of the world’s shorelines, limiting progress in coastal monitoring, hazard assessment, and numerical modeling. The Satellites to Shores (S2Shores) initiative addresses this gap by applying a wave-based inversion approach to more than one million Sentinel-2 images, producing a global coastal bathymetry dataset at 1-km spatial resolution. The open-source S2Shores pipeline integrates compositing, blending, and quality-control procedures optimized for large-scale, high-performance computing. Here we describe the dataset generation workflow, data curation steps, and global validation results. Comparisons against multibeam and chart-derived bathymetry show root mean square errors (RMSE) of 2–5 m and high consistency (<i>R</i><sup>2</sup>&#xa0;&gt;&#xa0;0.7) for depths between 10 and 40 m, where performance is most reliable. The effective retrieval depth varies regionally, reaching up to 60 m in energetic coastal environments. The dataset and accompanying source code are openly available, providing a reproducible global resource to support coastal research, climate studies, and operational forecasting systems.</p>

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Global 1-km Coastal Bathymetry from Sentinel-2 Wave Inversion using the Satellite-to-Shores (S2hores) Toolbox

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Rafael Almar,
  • Erwin W. J. Bergsma,
  • Grégoire Thoumyre,
  • Alain Giros,
  • Patrick Marchesiello,
  • Solange Lemai-Chenevier,
  • Stéphanie Artigues,
  • Sophie Loyer,
  • Jean-Marc Delvit

摘要

Reliable and up-to-date coastal bathymetry remains scarce for many of the world’s shorelines, limiting progress in coastal monitoring, hazard assessment, and numerical modeling. The Satellites to Shores (S2Shores) initiative addresses this gap by applying a wave-based inversion approach to more than one million Sentinel-2 images, producing a global coastal bathymetry dataset at 1-km spatial resolution. The open-source S2Shores pipeline integrates compositing, blending, and quality-control procedures optimized for large-scale, high-performance computing. Here we describe the dataset generation workflow, data curation steps, and global validation results. Comparisons against multibeam and chart-derived bathymetry show root mean square errors (RMSE) of 2–5 m and high consistency (R2 > 0.7) for depths between 10 and 40 m, where performance is most reliable. The effective retrieval depth varies regionally, reaching up to 60 m in energetic coastal environments. The dataset and accompanying source code are openly available, providing a reproducible global resource to support coastal research, climate studies, and operational forecasting systems.