融合多源遥感数据，实现巴基斯坦洪水的实时连续监测。
Continuous Flood Nowcasting in South Asia: A Multi-Sensor Ensemble Remote Sensing Framework for Flood Extent
- 构建多传感器集成框架，优先使用高分辨率卫星数据
- 生成2025年雨季每日连续洪水范围图，覆盖全境
- 支持灾情快速评估，适合应急响应与防灾规划
2025年6月至12月，巴基斯坦遭遇异常严重洪涝灾害，对人口、基础设施和农业造成连锁影响。现有业务洪水产品（如UNOSAT）虽提供关键事件快照，但难以在境内实现近实时、时空连续的淹没范围制图。本文提出一种基于多源遥感的连续洪水临近预报框架，整合哨兵-1 SAR、HLS L30/S30、MODIS 和 VIIRS 数据，在 Google Earth Engine 中统一网格化处理。该框架采用分层临近预报机制，优先使用高分辨率传感器（哨兵-1 和 HLS），必要时降级至 MODIS 与 VIIRS，确保各传感器原生分辨率下每日连续性。应用于2025年季风期，系统生成覆盖巴基斯坦的近实时、空间一致淹没图。以8月26日至9月7日超级洪灾为例，实现逐日追踪，突破传统事件式产品的时间局限。验证显示其与 GloFAS 流量异常及降水数据（CHIRPS v3.0, MSWEP）高度一致。结合暴露度图层（WorldPop, ESA WorldCover, Giga-HOTOSM），可快速估算受影响人口、耕地与关键设施，为南亚地区及时救灾与韧性规划提供支持。
原文摘要 · Abstract (English)
Pakistan experienced an unusually severe flood season between June and December 2025, with cascading impacts on population, infrastructure, and agriculture. Existing operational flood products (e.g., UNOSAT) provide valuable episode-level snapshots but rarely deliver spatially and temporally continuous inundation maps at near-real-time latency within the country. We present a multi-sensor, ensemble-based remote-sensing framework for continuous flood nowcasting in Pakistan that integrates Sentinel-1 SAR, Harmonized Landsat-Sentinel (HLS L30 and S30), MODIS, and VIIRS observations on a harmonized grid in Google Earth Engine. The framework employs a tiered nowcasting ensemble that prioritizes higher-resolution sensors (Sentinel-1 and HLS) and falls back to MODIS and VIIRS when necessary, preserving daily continuity of flood extent at each sensor's native resolution. Applied to the 2025 monsoon period, the system generates near-real-time, spatially consistent inundation maps across Pakistan. As a nowcasting case study, we track the super-flood of 26 August-7 September 2025 day by day, demonstrating the framework's ability to capture the evolving flood footprint in near real time and extend beyond the temporal limits of episodic mapping products. Validation against GloFAS discharge anomalies and precipitation datasets (CHIRPS v3.0, MSWEP) shows strong agreement with observed hydrometeorological conditions. By integrating nowcast outputs with exposure layers (WorldPop, ESA WorldCover, Giga-HOTOSM), the framework enables rapid estimation of affected populations, cropland, and critical infrastructure, supporting timely disaster response and resilience planning in South Asia.
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