Flood inundation Mapping for the Sept. 2023 Derna, Libya flood event using Sentinel-1 SAR data: An Integration of SAR intensity and interferometric coherence

Flood mapping, particularly in data-scarce regions, poses challenges including inadequate observational data to understand the hydrological characteristics of the floods. This study addresses this research gap by utilizing remotely sensed data, specifically Sentinel-1 Synthetic Aperture Radar (SAR) images, to delineate flood extent related to the September 11, 2023 Derna flood event in Libya. The objective is to extract flood extent from both SAR intensity and coherence and integrate these characteristics to generate a confidence flood map.

Our approach involves radiometric terrain correction of SAR data, flood pixel identification using anomaly detection techniques based on SAR intensity, and coherence analysis of pre-and post-flood SAR images. Flooded areas are categorized into 3 main classes. These include (1) High Confidence Flood (HCF), which is the intersection of SAR intensity and coherence in VV and VH bands in both Ascending and Descending directions; (2) Medium Confidence Flood (MCF), extracted from intensity and coherence in either the Ascending or Descending direction in both VV and VH bands; and (3) Low Confidence Flood (LCF), extracted from a single direction in either VV or VH band. LCF includes all pixels not confidently identified as part of either HCF or MCF.  The effectiveness of flood segmentation utilizing the integration of anomaly detection of SAR intensity and coherence analysis method is evaluated through a comparison between Sentinel-1 SAR data and optical Planet imagery.

Our findings indicate HCF covering approximately 8 hectares, MCF covering around 24 hectares, and LCF covering more than 227 hectares. These findings offer valuable insights into the observed flood extent at varying confidence levels. However, the moderate temporal resolution of Sentinel-1 data, with a revisit time of 12 days, introduces challenges in promptly detecting the entire extent of the flood. Overall, this study underscores the significance of remote sensing technology in near-real-time flood monitoring, emphasizing its role in identifying vulnerable areas, prioritizing resources, planning for potential risks, and supporting decision-making in relief efforts.