High-resolution 3-D flood information from radar imagery for flood hazard management

Guy Schumann*, Renaud Hostache, Christian Puech, Lucien Hoffmann, Patrick Matgen, Florian Pappenberger, Laurent Pfister

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

123 Citations (Scopus)


This paper presents a remote-sensing-based steady-state flood inundation model to improve preventive flood-management strategies and flood disaster management. The Regression and Elevation-based Flood Information extraction (REFIX) model is based on regression analysis and uses a remotely sensed flood extent and a high-resolution floodplain digital elevation model to compute flood depths for a given flood event. The root mean squared error of the REFIX, compared to groundsurveyed high water marks, is 18 cm for the January 2003 flood event on the River Alzette floodplain (G.D. of Luxembourg), on which the model is developed. Applying the same methodology on a reach of the River Mosel, France, shows that for some more complex river configurations (in this case, a meandering river reach that contains a number of hydraulic structures), piecewise regression is required to yield more accurate flood water-line estimations. A comparison with a simulation from the Hydrologic Engineering Centers River Analysis System hydraulic flood model, calibrated on the same events, shows that, for both events, the REFIX model approximates the water line reliably.

Original languageEnglish
Pages (from-to)1715-1725
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
Publication statusPublished - 1 Jun 2007


  • 1-D hydraulic model
  • Flood information mapping
  • Light detecting and ranging (lidar) digital elevation model (DEM)
  • Regression analysis
  • SAR data uncertainty
  • Synthetic aperture radar (SAR)

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