Evaluation of Sentinel-1 GRD Data with GEE for Floods Mapping in Rubkona, South Sudan

This study explores the integration of Sentinel-1 synthetic aperture radar (SAR) imagery and Google Earth Engine (GEE) for processing for flood mapping. The methodology involves the generation of flood and water masks through systematic processing of Sentinel-1 GRD data within the GEE platform. The RefineLEE algorithm is employed to reduce speckle noise and enhance the accuracy of flood extent delineation. The study includes a comprehensive validation process, incorporating ground truth data or independent sources to assess the accuracy of the derived flood and water masks. The validation aims to quantify the performance of the methodology in capturing flood extents against ground truth. Key outcomes include improved flood mapping accuracy backed by ground truth, which is a potential for operational flood monitoring. The combination of Sentinel-1 imagery and GEE processing demonstrate a robust methodology for mapping floods and can be used for providing valuable information for disaster management, environmental monitoring, and water resource management. This study contributes to flood mapping, offering a scalable and efficient approach for monitoring water-related events using freely available satellite data. The results highlight the significance of integrating SAR data processing with advanced algorithms and cloud-based platforms for accurate and timely flood mapping applications.