Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid areas from their analyses due to the sand and water confusion, resulting in a critical lack of observations which impedes response and recovery in these vulnerable regions. This paper explores the full potential of Sentinel-1 SAR to improve near-real-time flood mapping in arid and semi-arid regions. By investigating the impact of various parameters such as polarization, temporal information, and interferometric coherence, the most important information sources for detecting arid floods were identified. Using three distinct arid flood events in Iran, Pakistan, and Turkmenistan, different scenarios were constructed and tested using RF to evaluate the effectiveness of each feature. Permutation feature importance analysis was additionally conducted to identify key elements that reduce computational costs and enable a faster response during emergencies. Fusing VV coherence and amplitude information in pre-flood and post-flood imagery proved to be the most suitable approach. Results also show that leveraging crucial features reduces computational time by ∼35% as well as improves flood mapping accuracy by ∼50%. With advancements in cloud processing capabilities, the computational challenges associated with interferometric SAR computations are no longer a barrier. The demonstrated adaptability of the proposed approach across different arid areas, offers a step forward towards improved global flood mapping.

中文翻译：

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释放 Sentinel-1 在干旱地区洪水检测中的全部潜力


气候变化加剧了干旱和半干旱地区的洪水，给洪水监测和绘图带来了重大挑战。虽然卫星，特别是合成孔径雷达 （SAR），可以天气观测洪水范围，但准确区分沙地和干旱地区洪水的水仍然是一个公开的挑战。由于沙子和水的混淆，当前的全球洪水测绘产品将干旱地区排除在分析之外，导致严重缺乏观测，这阻碍了这些脆弱地区的响应和恢复。本文探讨了 Sentinel-1 SAR 在改进干旱和半干旱地区近乎实时洪水测绘方面的全部潜力。通过调查极化、时间信息和干涉相干性等各种参数的影响，确定了检测干旱洪水的最重要信息来源。使用伊朗、巴基斯坦和土库曼斯坦的三种不同的干旱洪水事件，使用 RF 构建和测试不同的场景，以评估每个特征的有效性。此外，还进行了排列特征重要性分析，以确定降低计算成本并在紧急情况下实现更快响应的关键要素。事实证明，在洪水前和洪水后的图像中融合 VV 相干性和振幅信息是最合适的方法。结果还表明，利用关键功能可将计算时间缩短约 35%，并将洪水测绘精度提高约 50%。随着云处理能力的进步，与干涉 SAR 计算相关的计算挑战不再是障碍。 所提出的方法在不同干旱地区被证明具有适应性，为改进全球洪水测绘提供了一步。