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<p>Floods are becoming increasingly frequent and severe due to climate change and rapid urbanization, posing significant threats to lives, infrastructure and economies. This review synthesizes recent advancements in Remote Sensing (RS) and Geographic Information System (GIS) technologies for comprehensive flood monitoring, mapping and management. A bibliometric and thematic analysis of 6154 publications from 2000 to 2023 was conducted using VOS viewer and Scopus-based filtering. The study focuses on RS platforms such as Landsat, Synthetic Aperture Radar (SAR) and Sentinel-1, as well as the integration of GIS-based hydrological models and emerging technologies. Findings reveal that over 75% of studies published after 2020 integrate SAR data with machine learning techniques for improved flood detection, while limitations persist with Landsat’s 30-metre resolution in urban areas. Light Detection and Ranging (LiDAR) and Sentinel-1 offer enhanced spatial accuracy, yet only 15% of studies address real-time forecasting challenges in data-scarce regions. Notably, SAR-based flood mapping exceeds 90% accuracy in 68% of analysed studies. This review highlights a critical research gap in integrating social vulnerability metrics and crowd-sourced data for rapid flood response. It recommends future work to prioritize the fusion of UAVs, IoT-based sensors, and artificial intelligence to develop real-time, sub-hourly flood alert systems, especially for densely populated megacities and under-resourced regions.</p>

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A review of remote sensing and GIS applications in flood monitoring and management

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Kempakkagari Bhargavi,
  • Dipankar Roy,
  • G Nakkeeran,
  • Narala Gangadhara Reddy

摘要

Floods are becoming increasingly frequent and severe due to climate change and rapid urbanization, posing significant threats to lives, infrastructure and economies. This review synthesizes recent advancements in Remote Sensing (RS) and Geographic Information System (GIS) technologies for comprehensive flood monitoring, mapping and management. A bibliometric and thematic analysis of 6154 publications from 2000 to 2023 was conducted using VOS viewer and Scopus-based filtering. The study focuses on RS platforms such as Landsat, Synthetic Aperture Radar (SAR) and Sentinel-1, as well as the integration of GIS-based hydrological models and emerging technologies. Findings reveal that over 75% of studies published after 2020 integrate SAR data with machine learning techniques for improved flood detection, while limitations persist with Landsat’s 30-metre resolution in urban areas. Light Detection and Ranging (LiDAR) and Sentinel-1 offer enhanced spatial accuracy, yet only 15% of studies address real-time forecasting challenges in data-scarce regions. Notably, SAR-based flood mapping exceeds 90% accuracy in 68% of analysed studies. This review highlights a critical research gap in integrating social vulnerability metrics and crowd-sourced data for rapid flood response. It recommends future work to prioritize the fusion of UAVs, IoT-based sensors, and artificial intelligence to develop real-time, sub-hourly flood alert systems, especially for densely populated megacities and under-resourced regions.