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Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling


The mapping of spatial inundation patterns during flood events is important for environmental management and disaster monitoring. Remote sensing technologies provide an affordable means of capturing flood extent with reasonable spatial and temporal coverage for flood monitoring. However, it is often difficult to provide an independent validation of the mapping algorithms with other remote sensing data since they can have similar issues. Hydrodynamic (HD) modelling tools are widely used for floodplain inundation modelling to a high accuracy, but they are resource intensive, making them impractical to use for large catchments. This paper looks at improving the mapping of flood events based on the daily MODIS Open Water Likelihood (OWL) algorithm (which provides the fraction of water within a pixel) by using information from HD modelling. It compares the MODIS OWL water fraction maps (500 m pixel size) with Landsat water maps (30 m pixel size) as well as those derived from two-dimensional HD modelling (at 150 m pixel size) to determine the best water fraction threshold at which a MODIS OWL pixel is identified as flooded. Comparison with Landsat water maps shows the best MODIS OWL water fraction threshold to vary depending on the spatial distribution of water. When compared to the HD model for water fraction thresholds of 0.1 (or 10 %) and below, our results show that values of 0.05–0.1 (or 5–10 %) were best. The overall cell-to-cell match is better when the 0.1 water fraction threshold is used; however, the total number of flooded pixels better matches with the 0.05 water fraction threshold. This study has shown that the simulated floods from calibrated two-dimensional HD models can be used to reduce the uncertainty in selecting a water fraction threshold in MODIS OWL images for producing regional-scale maps of flood events. The daily MODIS water maps (of 0.1 water fraction) were used to produce catchment-wide annual flood maps showing the maximum number of days that each pixel is inundated.

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Major funding for the Flinders and Gilbert Agricultural Resource Assessment was provided by the Office of Northern Australia and the Australian Government’s Northern Australia Sustainable Futures program. CSIRO co-invested through its Water for a Healthy Country and Sustainable Agricultural flagships.

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Correspondence to Catherine Ticehurst.

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Ticehurst, C., Dutta, D., Karim, F. et al. Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling. Nat Hazards 78, 803–820 (2015).

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  • Floodplain
  • Inundation mapping
  • Open Water Likelihood algorithm
  • Flinders catchment
  • Remote sensing
  • Disaster risk management
  • Monitoring