Development of flood detection framework integrating Synthetic Aperture Radar polarimetry and machine learning for semi-urban vegetation systems.

Semi-urban vegetation system includes vegetation partly managed by humans and partly growing naturally. It plays a vital role in ecosystem stability but are vulnerable to floods across the world highlighting the need for strategies to enhance resilience to climate change. However, detecting floods is difficult using Earth Observation due to complex scattering between radar signals and varied surface conditions. To address this, Synthetic Aperture Radar (SAR) polarimetry provides information to distinguish scattering patterns of flooded from non-flooded areas. The study proposes a flood detection methodology using Sentinel-1 SAR aimed at combining the Degree of Polarization (DOP) and Linear Polarization Ratio (LPR) derived from Stokes parameters, and Eigenvalues of the SAR covariance matrix. The proposed Flood Index (FI) integrates both amplitude and phase, unlike Normalized Difference Flood Index (NDFI) and VH/VV ratio that use only intensity data; the phase data helps separate smooth flooded surfaces from rough land or vegetation. A Random Forest model trained on the FI with bootstrap sampling detects flood extents accurately in Japan (2019 Typhoon Hagibis), India (2023 Delhi flood), and Greece (2023 Larissa flood). The model achieves F1 scores between 0.81 and 0.86 and Intersection over Union scores between 0.70 and 0.76. The proposed model is better than Otsu and NDFI across all study sites by maintaining lower False Negative Rate (0.09-0.17) and moderate False Positive Rate (0.19-0.39). Better transferability of the trained model is achieved across different flooded areas for scalable flood management in semi-urban vegetation areas.