Mapping floods using satellite data is crucial for managing and mitigating flood risks. Satellite imagery enables rapid and accurate analysis of large areas, providing critical information for emergency response and disaster management. Historical flood data derived from satellite imagery can inform long-term planning, risk management strategies, and insurance-related decisions. The Sentinel-1 satellite is effective for flood detection, but for longer time series, other satellites such as MODIS can be used in combination with deep learning models to accurately identify and map past flood events. We here develop a combined CNN--LSTM deep learning framework to fuse Sentinel-1 derived fractional flooded area with MODIS data in order to infer historical floods over Bangladesh. The results show how our framework outperforms a CNN-only approach and takes advantage of not only space, but also time in order to predict the fractional inundated area. The model is applied to historical MODIS data to infer the past 20 years of inundation extents over Bangladesh and compared to a thresholding algorithm and a physical model. Our fusion model outperforms both models in consistency and capacity to predict peak inundation extents.

翻译：利用卫星数据进行洪水测绘对于管理和减轻洪水风险至关重要。卫星图像能够快速准确地分析大范围区域，为应急响应和灾害管理提供关键信息。基于卫星图像的历史洪水数据可为长期规划、风险管理策略和保险相关决策提供依据。Sentinel-1卫星在洪水检测方面效果显著，但对于更长时间序列，可结合MODIS等其他卫星与深度学习模型精确识别和绘制过去洪水事件。本文开发了一种结合CNN-LSTM的深度学习框架，融合Sentinel-1导出的部分洪水面积与MODIS数据，以推断孟加拉国历史洪水情况。结果表明，该框架性能优于仅使用CNN的方法，不仅利用了空间信息，还充分利用时间信息预测部分洪水面积。该模型应用于历史MODIS数据，推断孟加拉国过去20年洪水范围，并与阈值算法和物理模型进行了比较。我们的融合模型在一致性及预测峰值洪水范围的能力方面均优于这两种模型。