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<p>The growing demand for precise, and high-frequency sea level monitoring has foster advances in alternative measurement techniques, with Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) emerging as a promising approach. This paper presents a novel algorithm for GNSS-IR sea level measurements, using an Extended Kalman Filter (EKF), which continuously estimates reflection heights, and allows sampling frequencies as high as those provided by the GNSS observations. To ensure continuous tracking of reflection heights in dynamic environmental conditions and prevent signal loss, the algorithm leverages multi-constellation GNSS observations and integrates tidal modeling. These features provide redundancy in data, significantly enhancing the robustness of signal tracking and improving the overall accuracy of sea level measurements. The algorithm was validated using data collected from three coastal sites with distinct environmental and hydrodynamic characteristics, Brest (France), Sète (France), and Cedar Key (USA). For each site, at least one year of data was analyzed at a high sampling frequency ranging from 20 to 30&#xa0;s. Across all sites, we achieved Root Mean Square Differences (RMSD) around 2.5&#xa0;cm with respect to conventional tide gauges. At Cedar Key, the algorithm successfully resolved a 70&#xa0;cm storm-induced sea level draw-down during Hurricane Milton in 2024, with an RMSD of 2.0&#xa0;cm, demonstrating the algorithm ability to accurately capture rapid sea level variations. With its accuracy, flexible resolution, and real-time capability, this cost-effective and scalable approach offers strong potential to fill geographical gaps where classic techniques are challenged.</p>

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Ground-based high-frequency sea level monitoring from multi-GNSS reflectometry using extended Kalman filtering

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Aurélien Pira,
  • Alvaro Santamaría-Gómez,
  • Guy Wöppelmann

摘要

The growing demand for precise, and high-frequency sea level monitoring has foster advances in alternative measurement techniques, with Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) emerging as a promising approach. This paper presents a novel algorithm for GNSS-IR sea level measurements, using an Extended Kalman Filter (EKF), which continuously estimates reflection heights, and allows sampling frequencies as high as those provided by the GNSS observations. To ensure continuous tracking of reflection heights in dynamic environmental conditions and prevent signal loss, the algorithm leverages multi-constellation GNSS observations and integrates tidal modeling. These features provide redundancy in data, significantly enhancing the robustness of signal tracking and improving the overall accuracy of sea level measurements. The algorithm was validated using data collected from three coastal sites with distinct environmental and hydrodynamic characteristics, Brest (France), Sète (France), and Cedar Key (USA). For each site, at least one year of data was analyzed at a high sampling frequency ranging from 20 to 30 s. Across all sites, we achieved Root Mean Square Differences (RMSD) around 2.5 cm with respect to conventional tide gauges. At Cedar Key, the algorithm successfully resolved a 70 cm storm-induced sea level draw-down during Hurricane Milton in 2024, with an RMSD of 2.0 cm, demonstrating the algorithm ability to accurately capture rapid sea level variations. With its accuracy, flexible resolution, and real-time capability, this cost-effective and scalable approach offers strong potential to fill geographical gaps where classic techniques are challenged.