A multi-sensor framework for comparing and integrating optical, C-band and L-band SAR inundation products in tropical floodplain of Australia

Accurate inundated landscape mapping is essential for understanding hydrological connectivity, wetland dynamics and floodplain processes in tropical environments. However, optical and Synthetic Aperture Radar (SAR) sensors often produce inconsistent inundation estimates due to differences in sensor physics, wavelength sensitivity, vegetation and cloud penetration, and analytical methods. These challenges are amplified in wet–dry tropical systems, where cloud cover, heterogeneous vegetation and seasonal flooding complicate water detection. This study presents a unified, data-driven framework for comparing inundation products derived from Sentinel-2 optical imagery, Sentinel-1 C-band and PALSAR-2 L-band SAR in a tropical floodplain system in northern Australia. Inundated landscape maps were generated using supervised Random Forest classification and unsupervised multi-Otsu thresholding, followed by multi-sensor fusion analysis. Products were validated using wet- and dry-season field observations. Results showed strong seasonal and sensor-specific differences. Sentinel-2 provided the most balanced representation of dry-season floodplain vegetation and inundation, while Sentinel-1 achieved high wet-season accuracy (>0.95) under cloud-affected conditions. PALSAR-2 improved the detection of inundated vegetation and structurally complex environments, reaching an overall accuracy of 0.97. Multi-sensor fusion generally improved spatial coherence and class delineation, but performance varied across sensor combinations. These findings show that optical, C-band SAR and L-band SAR inundation products are not directly interchangeable in tropical wetlands. The proposed framework supports more robust interpretation, comparison and integration of multi-sensor inundation products for wetland monitoring, hydrological assessment and environmental management in data-limited tropical regions.