Lin et al. (2025) Monitoring surface water in floodplains by satellites: Progress, challenges, and perspectives

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-10-25
• Authors: Yaling Lin, Chunqiao Song
• DOI: 10.1016/j.jhydrol.2025.134458

Research Groups

• State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
• University of Chinese Academy of Sciences, Beijing, China
• University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing, China

Short Summary

This review systematically summarizes advancements in satellite remote sensing for monitoring floodplain hydrological variables, identifies current challenges, and proposes a future multi-source, multi-variable, and automated framework for improved global-scale monitoring.

Objective

• To provide a systematic and multi-dimensional summary of advancements in remote sensing data sources and monitoring methodologies for various floodplain hydrological variables, including water inundation extent, level, depth, volume, underwater topography, and hydrological connectivity, from a macro perspective.
• To identify overarching challenges in multi-variable monitoring and chart key future research trajectories.

Study Configuration

• Spatial Scale: Regional to global scales.
• Temporal Scale: Near-real-time, long-term observations.

Methodology and Data

• Models used: Optical-SAR integration, deep learning, and ancillary data are key methodologies for complex inundation detection.
• Data sources: Satellite remote sensing (general), SWOT (Surface Water and Ocean Topography), Sentinel data. A bibliometric survey of 826 studies was conducted.

Main Results

• A bibliometric survey of 826 studies revealed that over 70% focused on water inundation extent, with hotspots in the central Amazon and the middle-lower Yangtze Basin.
• Optical-SAR integration is the dominant approach, with deep learning and ancillary data significantly improving complex inundation detection.
• Significant challenges remain in capturing flood peaks and monitoring small floodplain waterbodies.
• Studies on three-dimensional variables (e.g., depth, volume) are scarce due to limitations in pixel-scale water levels and high-precision digital elevation models (DEMs).
• Existing global floodplain extent products overlap by less than 40% and often miss complex inundation patterns, even at 10 meter resolution.
• Pixel-scale water levels from SWOT offer a transformative opportunity for large-scale, multi-dimensional monitoring.
• The study stresses the need for a multi-source, multi-variable, and automated framework combining SWOT and Sentinel data with deep learning on cloud platforms to enable generalized near-real-time, high-precision, and global three-dimensional monitoring of floodplain hydrological variables.

Contributions

• Provides the first global-scale, systematic, and multi-dimensional review of advancements in satellite remote sensing for floodplain water monitoring.
• Identifies critical gaps and challenges in current methodologies, particularly for three-dimensional variables and small floodplain waterbodies.
• Proposes a novel, integrated framework leveraging emerging technologies (SWOT, deep learning, cloud platforms) to advance global, multi-variable, near-real-time floodplain hydrological monitoring, supporting hydrodynamic modeling, flood warning, and risk management.

Funding

• Not specified in the provided text.

Citation

@article{Lin2025Monitoring,
  author = {Lin, Yaling and Song, Chunqiao},
  title = {Monitoring surface water in floodplains by satellites: Progress, challenges, and perspectives},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134458},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134458}
}