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<p>Intermittent rivers and ephemeral streams (IRES), also known as non-perennial river segments (NPRs), have garnered attention due to their significant roles in watershed hydrology and ecosystem services, especially in the context of climate change and escalating human activities. Recent advances in machine learning (ML) techniques have significantly improved the analysis of dynamic changes in IRES. Various ML models, including random forest (RF), long short-term memory (LSTM), and U-Net, demonstrate clear advantages in processing complex hydrological data, enhancing the efficiency and accuracy of IRES extraction from remote sensing data. Furthermore, hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms. However, ML methods still face challenges, including high data dependence, computational complexity, and scalability issues with models. This review proposes an IRES monitoring framework that combines satellite data with ML algorithms, integrating remote sensing technologies such as optical imaging and synthetic aperture radar, and evaluates the advantages and limitations of different ML methods. It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics, conduct ecological assessments, and support sustainable water management, offering a scientific foundation for addressing environmental and anthropogenic pressures.</p>

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A review of dynamic monitoring methods for intermittent rivers: Integrating remote sensing and machine learning

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Chaoshuai Xie,
  • Aifeng Lv

摘要

Intermittent rivers and ephemeral streams (IRES), also known as non-perennial river segments (NPRs), have garnered attention due to their significant roles in watershed hydrology and ecosystem services, especially in the context of climate change and escalating human activities. Recent advances in machine learning (ML) techniques have significantly improved the analysis of dynamic changes in IRES. Various ML models, including random forest (RF), long short-term memory (LSTM), and U-Net, demonstrate clear advantages in processing complex hydrological data, enhancing the efficiency and accuracy of IRES extraction from remote sensing data. Furthermore, hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms. However, ML methods still face challenges, including high data dependence, computational complexity, and scalability issues with models. This review proposes an IRES monitoring framework that combines satellite data with ML algorithms, integrating remote sensing technologies such as optical imaging and synthetic aperture radar, and evaluates the advantages and limitations of different ML methods. It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics, conduct ecological assessments, and support sustainable water management, offering a scientific foundation for addressing environmental and anthropogenic pressures.