Flooding is one of the most severe natural hazards, leading to significant losses in human life, infrastructure, and economic resources, particularly in flood-prone regions such as India. Rapid and reliable identification of inundated areas is essential for effective disaster response, mitigation planning, and resource allocation. Conventional flood mapping techniques are often labor-intensive, time-consuming, and limited by environmental constraints. In particular, optical satellite imagery is highly affected by cloud cover and poor visibility during extreme weather conditions. To address these limitations, this study proposes an automated flood assessment framework utilizing satellite-based remote sensing data. The approach primarily leverages Synthetic Aperture Radar (SAR) imagery, which enables consistent data acquisition irrespective of weather conditions or illumination. The proposed framework integrates image preprocessing, change detection, and region extraction techniques to identify flood-affected areas by analyzing temporal variations between pre-event and post-event images. The system is designed to efficiently highlight newly formed water bodies and quantify flood impact through statistical and visual outputs. A web-based interface is incorporated to enhance accessibility and interpretation of results. Experimental observations demonstrate that the proposed method provides reliable flood detection across diverse terrains, including urban and vegetation-covered regions. This work contributes toward developing a scalable and efficient solution for large-scale flood monitoring, supporting timely decision-making and improving disaster management strategies.
Senbagam et al. (Thu,) studied this question.