AFFLUX: Disaster Management Using Multi-Temporal Sentinel-1 Data

Abstract

Accurate flood mapping plays a pivotal role in disaster management and mitigation endeavors. This research paper presents an innovative change detection approach utilizing Synthetic Aperture Radar (SAR) Sentinel-1 data for precise flood extent mapping. The proposed methodology harnesses the robust capabilities of the Google Earth Engine platform for seamless satellite imagery processing. Focused on a specific region, the adaptable code showcased herein caters to diverse study areas and various flood events. The script efficiently filters and pre- processes Sentinel-1 Ground Range Detected (GRD) data, selects pre and post-flood images, applies advanced speckle filtering techniques, and precisely clips the images to the designated study area. Flood extent is then accurately calculated by computing the disparity between the pre and post-flood images, employing a discerning difference threshold, and refining the result through integration with additional datasets, such as surface water seasonality and high-resolution Digital Elevation Model (DEM). Moreover, the code encompasses an in-depth damage assess- ment component by thoroughly analyzing exposed population density, impacted agricultural land, and affected urban areas. The findings pertaining to exposed population, compromised cropland, and urban region are effectively summarized and visually presented on the resulting map. Thus, this comprehensive code provides an invaluable tool for flood mapping and damage assessment, facilitating timely and effective disaster response measures. Experimental results robustly validate the efficacy and versatility of the proposed approach.