卫星洪水监测效果受地表类型和洪水类型影响，农田最易识别，林地建筑区几乎无法检测。
Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
- 用普里蒂维-EO-2.0模型在19个全球洪水事件上测试，覆盖六大洲八气候带
- 农田洪水检测准确率最高（IoU=52%），林地与城区检测几乎失效（IoU=4%）
- 发现参考数据不一致是误判主因之一，适合灾害响应与遥感系统优化者阅读
洪水是破坏性最强的自然灾害之一，气候变化下频率上升使得基于卫星的淹没范围监测对灾后响应至关重要。利用预训练于卫星档案的地理空间基础模型具备地理迁移能力，但其在多样且未见过的事件中运行可靠性尚不清楚。本研究将普里蒂维-EO-2.0应用于19个分布外洪水事件（2017–2025年），涵盖六大陆、八气候带及六种洪水机制，对照两个独立参考产品进行验证。检测精度同时受地表覆盖与洪水类型影响：农田达到最高一致性（IoU=52%），河流型洪水检测最强（F1=0.69），而林地与建成区无论何种洪水机制，检测率均接近零（IoU=4%）。双参考验证表明，模型看似误差部分源于参考产品定义不一致，而非检测失败。迭代流程测试识别出23种故障模式，其中流程工程问题导致的初始错误远超模型能力限制。该研究确立了操作性卫星洪水监测的环境依赖边界。
原文摘要 · Abstract (English)
Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response. Geospatial foundation models pretrained on satellite archives offer geographic transferability, but their operational reliability across diverse, unseen events remains uncharacterized. Here we deploy Prithvi-EO-2.0 across 19 out-of-distribution flood events (2017-2025) spanning six continents, eight climate zones, and six flood mechanisms, validating against two independent reference products. Detection accuracy depended jointly on land cover and flood type, with cropland yielding the highest agreement (IoU=52%) and riverine events the strongest detection (F1=0.69), while tree cover and built-up areas showed near-zero detection (IoU=4%) regardless of flood mechanism. Dual-reference validation revealed that apparent model error partly reflects definitional inconsistency between reference products rather than detection failure. Iterative pipeline testing identified 23 failure modes, with pipeline engineering dominating initial error over model capacity. These findings establish environment-dependent detection boundaries for operational satellite flood mapping.
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