Amplified deviation flood index (ADFI) for fast non-prior flood detection

Abstract

Climate change causes widespread increases in the frequency, magnitude, and extent of flood events, which pose increasing threats to societal and natural systems and highlight the urgency for timely and accurate flood mapping. However, previous flood mapping methods often require prior knowledge (such as the timing and location) of flood events that is usually incomplete or even unavailable when studying historical floods. Here we propose a new amplified deviation flood index (ADFI) using the time-series anomaly statistics from the Synthetic Aperture Radar (SAR) data for mapping fully flooded areas without relying on prior knowledge of flood events. ADFI is constructed by considering two fundamentals of flood events: a decrease in backscatter intensity when ground objects are fully flooded and an increase in the variance of backscatter intensity owing to infrequently sudden occurrence of flood events, thus enabling a fast non-prior detection of flood events and extents. The performance of ADFI is assessed in four study areas across different climate zones of the globe, and the assessment shows that the overall accuracies of ADFI in all study areas exceed 93%, with precision >95% and recall >94%. Further comparison with two existing flood indices suggests that our proposed ADFI-based mapping method can improve the overall accuracy by 12.11%–3.97%, precision by 12.59%–10.17%, and recall by 54.32%–6.37%. A time-series flood mapping based on ADFI demonstrates that our proposed method enables a non-prior, precise, and fast detection of flood events and allows prompt monitoring of flood disasters. Our proposed approach enhances the efficiency and scalability of flood monitoring, providing a valuable tool for rapid disaster response and the reconstruction of long-term flood histories across diverse environments and climates.