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<p>Floods are one of the most devastating natural disasters, causing widespread damage to life, property, and ecosystems. Real-time flood monitoring continues to be a challenge due to inefficient evaluation techniques and insufficient field data. Existing techniques are often time-consuming, low in accuracy, or lack scalability. This research proposes a remote sensing-based flood assessment framework with Sentinel-1 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. The combined use of Sentinel-1’s all-weather, day-and-night radar capabilities and Sentinel-2’s high-resolution optical data increases the accuracy, consistency, and clarity of flood identification and damage evaluation. Our methodology includes data preprocessing, flood extent mapping, and Land Use Land Cover (LULC)-based impact analysis to identify inundated areas. Through the integration of 11 LULC classes, the research yields in-depth knowledge on flood effects on various categories of land including agriculture, urban areas, forest, and water bodies. The outputs are spatial maps of floods as well as statistical reports, making it possible for timely and high-resolution monitoring. The results demonstrate improved delineation of floods as well as flood-induced damage insight. This research enhances flood risk management to inform policymakers and disaster response units to make informed decisions-making.</p>

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A remote sensing approach for flood extent estimation and damage mapping in the Bramhani-Baitarani river basin using Sentinel data

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Sonali Kadam,
  • Ravindra Kale,
  • Anjali Kadam,
  • Roshnadevi Sapkal,
  • Vaishnavi Thorat,
  • Gayatri Kavade,
  • Trupti Pokharkar,
  • Gayatri Temgire,
  • Jotiram Gujar

摘要

Floods are one of the most devastating natural disasters, causing widespread damage to life, property, and ecosystems. Real-time flood monitoring continues to be a challenge due to inefficient evaluation techniques and insufficient field data. Existing techniques are often time-consuming, low in accuracy, or lack scalability. This research proposes a remote sensing-based flood assessment framework with Sentinel-1 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. The combined use of Sentinel-1’s all-weather, day-and-night radar capabilities and Sentinel-2’s high-resolution optical data increases the accuracy, consistency, and clarity of flood identification and damage evaluation. Our methodology includes data preprocessing, flood extent mapping, and Land Use Land Cover (LULC)-based impact analysis to identify inundated areas. Through the integration of 11 LULC classes, the research yields in-depth knowledge on flood effects on various categories of land including agriculture, urban areas, forest, and water bodies. The outputs are spatial maps of floods as well as statistical reports, making it possible for timely and high-resolution monitoring. The results demonstrate improved delineation of floods as well as flood-induced damage insight. This research enhances flood risk management to inform policymakers and disaster response units to make informed decisions-making.