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<p>Flood monitoring and water level estimation are essential for effective flood management but remain challenging due to sparse station distribution and complex nonlinear dynamics. This study proposes a Sentinel-1 SAR-based machine learning framework that integrates Sentinel-1 observations with a water-level-conditioned Long Short-Term Memory (LSTM) modeling for water level estimation in the Yeongsan River Basin, South Korea. Water body detection used Otsu thresholding and K-means clustering and validated against ESA WorldCover maps. Both methods achieved high accuracy (0.88–0.90) with low false alarm rates (0.02–0.04). K-means outperformed Otsu across most sites, while Otsu showed better performance at tributary stations with small water bodies due to reduced class imbalance. However, both methods exhibited reduced accuracy along land–water boundaries where mixed scattering was prevalent. For water level estimation, Sentinel-1 backscatter (σ⁰<sub>VV</sub>, σ⁰<sub>VH</sub>), incidence angle, and day of year were used as inputs to the LSTM model. Model performance exhibited pronounced spatial heterogeneity: tributary sites governed primarily by rainfall-driven hydrological conditions achieved superior performance (R up to 0.89; RMSE &lt; 0.20&#xa0;m), whereas mainstream sites influenced by dams and weirs showed limited accuracy (<i>R</i> &lt; 0.30). To address hydrologic nonstationary and data imbalance, a water-level-conditioned training strategy was introduced by splitting datasets into high- and low-water level conditions. This approach enhanced model performance at several mainstream sites, although underestimation of peak levels remained due to data imbalance and the relatively low temporal resolution of SAR observations. Overall, the results demonstrate the potential of integrating SAR observations with machine learning for water body detection and water level estimation. The proposed framework provides a practical and data-efficient approach for hydrological monitoring and flood risk management, particularly in data-sparse and hydrologically complex river systems.</p>

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Sentinel-1 and LSTM-based water level estimation in a regulated river basin

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Yangwan Kim,
  • Dongkyun Kim,
  • Kyuhyun Byun,
  • Jongmin Park

摘要

Flood monitoring and water level estimation are essential for effective flood management but remain challenging due to sparse station distribution and complex nonlinear dynamics. This study proposes a Sentinel-1 SAR-based machine learning framework that integrates Sentinel-1 observations with a water-level-conditioned Long Short-Term Memory (LSTM) modeling for water level estimation in the Yeongsan River Basin, South Korea. Water body detection used Otsu thresholding and K-means clustering and validated against ESA WorldCover maps. Both methods achieved high accuracy (0.88–0.90) with low false alarm rates (0.02–0.04). K-means outperformed Otsu across most sites, while Otsu showed better performance at tributary stations with small water bodies due to reduced class imbalance. However, both methods exhibited reduced accuracy along land–water boundaries where mixed scattering was prevalent. For water level estimation, Sentinel-1 backscatter (σ⁰VV, σ⁰VH), incidence angle, and day of year were used as inputs to the LSTM model. Model performance exhibited pronounced spatial heterogeneity: tributary sites governed primarily by rainfall-driven hydrological conditions achieved superior performance (R up to 0.89; RMSE < 0.20 m), whereas mainstream sites influenced by dams and weirs showed limited accuracy (R < 0.30). To address hydrologic nonstationary and data imbalance, a water-level-conditioned training strategy was introduced by splitting datasets into high- and low-water level conditions. This approach enhanced model performance at several mainstream sites, although underestimation of peak levels remained due to data imbalance and the relatively low temporal resolution of SAR observations. Overall, the results demonstrate the potential of integrating SAR observations with machine learning for water body detection and water level estimation. The proposed framework provides a practical and data-efficient approach for hydrological monitoring and flood risk management, particularly in data-sparse and hydrologically complex river systems.