Improving sentinel-1 flood maps using a topographic index as prior in Bayesian inference

Abstract

Sentinel-1-based flood mapping works well but with well-known issues over rugged terrain. Applying exclusion masks to improve the results is common practice in unsupervised and global applications. One such mask is the height above the nearest drainage (HAND), which uses terrain information to reduce flood lookalikes in SAR images. The TU Wien flood mapping algorithm is one operational workflow using this mask. Being a Bayesian method, this algorithm can integrate auxiliary information as prior probabilities to improve classifications. This study improves the TU Wien flood mapping algorithm by introducing a HAND prior function instead of using it as a mask. We estimate the optimal function parameters and observe the performance in flooded and non flooded scenarios in six study sites. We compare the flood maps generated with HAND and (baseline) non-informed priors with reference CEMS rapid mapping flood extents. Our results show enhanced performance by decreasing false negatives at the cost of slightly increasing false positives. In utilizing a single parametrization, the improved algorithm shows potential for global implementation.