Utilization of Open-Source SAR Data for Flood Detection: Insights from the Yamuna River Basin Flood

Abstract

In early July 2023, the Yamuna River Basin in Delhi experienced severe flooding, primarily triggered by high-intensity rainfall and flash releases at the Okhla Barrage. The combination of rapid urbanization, impervious surfaces, and inadequate drainage infrastructure exacerbated flood impacts, leading to extensive inundation, property damage, and disruption of livelihoods. Accurate and timely flood mapping is essential for mitigating such urban flood risks and enhancing disaster preparedness. In this study, Sentinel-1 SAR data acquired before and during the flood were utilized to delineate inundated areas. The inherent sensitivity of SAR to surface water, due to reduced backscatter from specular reflection, makes it a reliable tool for identifying flood-prone zones. Flood extent was extracted through preprocessing and threshold-based band math operations. Additionally, Sentinel-2 harmonized data and Dynamic World classifications were integrated to assess Land Use/Land Cover (LULC) changes in the affected region. A coherence analysis using pre- and post-flood datasets further supported the detection of inundated zones. Results revealed significant impacts of flooding on different LULC classes, highlighting the vulnerability of built-up and agricultural areas. The study demonstrates the effectiveness of open-source SAR and optical datasets for flood detection and mapping in urban river basins, thereby offering a cost-effective and reliable approach to strengthen disaster response and urban resilience planning.