| 03 Feb 2026
Large-scale flood monitoring based on time series Sentinel-1 images and Z-index
Abstract. Synthetic Aperture Radar (SAR) satellites have emerged as a crucial technology for real-time flood monitoring in the face of escalating flood disaster hazards. However, current flood extraction techniques still have a lot of drawbacks when it comes to solving the twin problems of accurate urban flood detection and extensive monitoring. To address this challenge, this study proposes a novel approach based on an analysis of backscatter characteristics across different land cover types, combined with multiple auxiliary datasets, enabling effective monitoring of both extensive flooding and inundation in building areas. First, an analysis of scattering behavior in time-series SAR images revealed that in natural areas, the consistency of backscatter intensity is strongly influenced by vegetation growth status. In urban areas, rainfall can intensify double-bounce scattering, also disrupting intensity consistency. Based on these findings, a Z-score-based flood classification tree was developed. This method uses reference images and flood-period images to compute Z-score maps, enabling pixel-level flood probability estimation and establishing flood detection thresholds with clear statistical significance. The integration of VV and VH polarizations within the classification tree further improves the reliability of flood identification. Applied to the 2021 Weihui flood event, the method demonstrated strong performance, achieving a critical success index (CSI) of 60 % and overall accuracy (OA) of 90 % in natural areas, and a CSI of 62 % and OA of 73 % in building areas. The proposed approach shows significant advantages in accurately classifying flood-affected areas and offers the capability to monitor both large-scale floods and urban inundation.
As a reviewer, I find this paper timely and relevant, since it addresses the important problem of combining large-scale flood mapping with urban inundation detection using Sentinel-1 time series. The main contribution is the proposed Z-score-based classification tree that uses VV/VH polarizations together with auxiliary datasets to distinguish flood responses in natural and built-up areas. The Weihui 2021 case study shows that the method is reasonably effective, with strong overall accuracy in natural areas and moderate but useful performance in urban areas. The paper is also valuable because it discusses the physical scattering mechanisms behind the different responses of cropland, water, and buildings, rather than presenting the method as a purely empirical workflow. Overall, the manuscript is promising for publishing in HESS. I will put some minor comments: