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

Abstract

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