Zhang et al. (2026) Amplified deviation flood index (ADFI) for fast non-prior flood detection

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-01-19
• Authors: Hui Zhang, Ming Luo, Zhixin Qi, Xing Li, Yongquan Zhao
• DOI: 10.1016/j.rse.2026.115258

Research Groups

• Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
• State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China

Short Summary

This paper introduces the Amplified Deviation Flood Index (ADFI), a novel method utilizing time-series Synthetic Aperture Radar (SAR) data for fast, non-prior detection and mapping of fully flooded areas. The ADFI demonstrates high accuracy (overall accuracy >93%) across diverse global climate zones and significantly outperforms existing flood indices.

Objective

• To develop and validate a new Amplified Deviation Flood Index (ADFI) using time-series Synthetic Aperture Radar (SAR) data for fast, non-prior detection and mapping of fully flooded areas, without requiring prior knowledge of flood event timing or location.

Study Configuration

• Spatial Scale: Four study areas across different climate zones globally.
• Temporal Scale: Time-series analysis for anomaly statistics and flood mapping, enabling monitoring of flood events and reconstruction of long-term flood histories.

Methodology and Data

• Models used: Amplified Deviation Flood Index (ADFI), which considers a decrease in backscatter intensity and an increase in backscatter intensity variance during flood events.
• Data sources: Synthetic Aperture Radar (SAR) data, specifically Sentinel-1.

Main Results

• The ADFI achieved overall accuracies exceeding 93% across all study areas, with precision greater than 95% and recall greater than 94%.
• Compared to two existing flood indices, the ADFI-based mapping method improved overall accuracy by 3.97% to 12.11%, precision by 10.17% to 12.59%, and recall by 6.37% to 54.32%.
• The proposed method enables non-prior, precise, and fast detection of flood events, facilitating prompt monitoring of flood disasters.

Contributions

• Introduction of a novel Amplified Deviation Flood Index (ADFI) that enables flood detection and mapping without requiring prior knowledge of flood event timing or location.
• The ADFI is uniquely constructed by integrating both backscatter intensity decrease and variance increase, addressing key characteristics of flood events in SAR data.
• Demonstrated significant performance improvements (up to 12.11% in overall accuracy, 12.59% in precision, and 54.32% in recall) compared to existing flood indices.
• 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.

Funding

• Not available in the provided text.

Citation

@article{Zhang2026Amplified,
  author = {Zhang, Hui and Luo, Ming and Qi, Zhixin and Li, Xing and Zhao, Yongquan},
  title = {Amplified deviation flood index (ADFI) for fast non-prior flood detection},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115258},
  url = {https://doi.org/10.1016/j.rse.2026.115258}
}