An Intercomparison of Sentinel-1 Based Change Detection Algorithms for Flood Mapping

With its unrivaled and global land monitoring capability, the Sentinel-1 mission has been established as a prime provider in SAR-based flood mapping. Compared to suitable single-image algorithms, change-detection methods offer better robustness, retrieving extent from classification of observed changes. This requires data-based parametrization. Moreover, scope automatic services, employed algorithms should not rely on locally optimized parameters, which cannot be automatically estimated have spatially varying quality, impacting much mapping accuracy. Within recently launched Global Flood Monitoring (GFM) service, we implemented Bayes-Inference (BI)-based algorithm designed meet these ends. However, whether other change detection perform similarly or is unknown. study examines four models: The Normalized Difference Scattering Index (NDSI), Shannon’s entropy NDSI (SNDSI), Standardized Residuals (SR), Bayes Inference over Luzon Philippines, was flood-hit by typhoon November 2020. After parametrization assessment against an expert-created map, models are inter-compared independent Sentinel-2 classification. obtained findings indicate that profits scalable rules shows least sensitivity choices while also performing best terms For all models, backscatter seasonality model for no-flood reference delivered results.