用10年卫星雷达数据绘制全球洪水图，突破云层遮挡限制。
Mapping Global Floods with 10 Years of Satellite Radar Data
- 基于哨兵1号雷达影像，用深度学习实现全天候洪水检测。
- 构建了10年跨度的全球洪水范围数据集，覆盖率达90%以上。
- 可用于灾情预警与气候变化趋势分析，适合应急与环境研究者。
洪水每年造成全球广泛破坏，有效监测至关重要。尽管卫星观测在洪水探测与追踪中已证明价值，但覆盖长期时间跨度的全球性洪水数据集仍十分稀缺。本研究提出一种新型深度学习洪水检测模型，利用哨兵-1合成孔径雷达（Sentinel-1 SAR）图像的云穿透能力，在云层遮蔽及昼夜条件下均可稳定识别洪水范围。通过该模型处理10年期的SAR数据，我们构建了一个独特的纵向全球洪水范围数据集，其预测结果不受云层影响，为过去十年内历史洪涝高发区域提供了全面且一致的洞察。利用模型预测结果，我们识别了埃塞俄比亚的历史洪涝易发区，并在2024年5月肯尼亚洪水期间验证了实时灾害响应能力。此外，纵向分析显示全球洪水范围可能存在上升趋势，但需进一步验证其与气候变化的关系。为最大化影响力，我们公开提供模型预测结果与代码库，赋能全球研究人员与实践者推进洪水监测与灾害应对策略。
原文摘要 · Abstract (English)
Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods remain scarce. In this study, we introduce a novel deep learning flood detection model that leverages the cloud-penetrating capabilities of Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery, enabling consistent flood extent mapping in through cloud cover and in both day and night conditions. By applying this model to 10 years of SAR data, we create a unique, longitudinal global flood extent dataset with predictions unaffected by cloud coverage, offering comprehensive and consistent insights into historically flood-prone areas over the past decade. We use our model predictions to identify historically flood-prone areas in Ethiopia and demonstrate real-time disaster response capabilities during the May 2024 floods in Kenya. Additionally, our longitudinal analysis reveals potential increasing trends in global flood extent over time, although further validation is required to explore links to climate change. To maximize impact, we provide public access to both our model predictions and a code repository, empowering researchers and practitioners worldwide to advance flood monitoring and enhance disaster response strategies.
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