Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
For disaster response agencies, this work establishes operational detection boundaries for satellite flood mapping, revealing that model reliability is highly environment-dependent.
The study evaluates the satellite-based flood mapping model Prithvi-EO-2.0 across 19 diverse flood events, finding that detection accuracy is jointly governed by land cover and flood type, with cropland achieving highest agreement (IoU=52%) and tree cover/built-up areas near-zero detection (IoU=4%).
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.