Assessment of time-series-derived no-flood references for sar-based Bayesian flood mapping

Abstract

ABSTRACT The systematic mapping of flood events with Synthetic Aperture Radar (SAR) data is an area of growing importance. One global flood mapping algorithm utilized within the Copernicus Emergency Management Service is based upon a Bayesian Inference model that compares a SAR image to a simulated reference image representing no-flood conditions. This no-flood reference image is at present generated using a harmonic model trained using historic time series, thereby producing a backscatter image representing mean seasonal conditions. One known weakness of this approach is that it cannot account for changing environmental conditions from year to year, potentially causing an overestimation of flood extent during dry periods, snow and frost, or other effects causing lower-than normal backscatter. To minimize this detrimental effect, we introduce an exponential filter to estimate the no-flood reference image by weighting the most recent backscatter observations according to their time difference to the current SAR acquisition. We compare the performance of the new exponential filter model against the harmonic model using a novel time-series flood mapping assessment approach. First, we assess their predictions against the actual SAR image time series for the year 2023. Then, we analyze the false positive rate of the corresponding flood maps generated to ensure the robustness of the automated algorithm outside of flood events. Furthermore, we perform qualitative and quantitative analyses of flood maps matching with semi-automatic results from Copernicus Emergency Management Services and Sentinel Asia as a reference. Our time-series analysis confirms increased false positive rates due to well-known environmental drivers and highlights issues with agricultural overestimation. In this regard, the time-series comparisons of the no-flood reference models show a clear improvement in the TU Wien algorithm with the exponential filter, effectively reducing false positive rates on non-flooded scenes in most study sites. The exponential filter performed better than the harmonic model in most flooded scenes, where sites show generally improved Critical Success Index and User’s accuracy. However, the exponential filter model has difficulties with sites with prolonged floods in the time series, requiring further development. Overall, the exponential filter no-flood reference model shows great promise for improved global near-real-time flood mapping.