Rodriguez_et_al_SWOT_IGARSS2011.ppt

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  • 1. INTERFEROMETRIC PROCESSING OF FRESH WATER BODIES FOR SWOT Ernesto Rodríguez, JPL/CalTech Delwyn Moller, Remote Sensing Solution Xiaoqing Wu, JPL/CalTech Kostas Andreadis, JPL/CalTech
  • 2. Study Area
    • ~1000 km reach of the Ohio River basin
    • Drains an area of ~220,000 km 2
    • Topography from National Elevation Dataset (30 m)
    • River vector maps from HydroSHEDS
    • Channel width and depth from developed power-law relationships
    • Explicitly modeled rivers with mean widths at least 50 m
  • 3. Hydrodynamic Modeling
    • LISFLOOD-FP raster-based model
    • 1-D solver for channel flow
    • 2-D flood spreading model for floodplain flow
    • Kinematic, Diffusive and Inertial formulations
    • Requires information on topography, channel characteristics and boundary inflows
    • Needed to coarsen spatial resolution to 100 m
    SWOT Hydrology Virtual Mission Meeting, Paris, 22 Sep 2010
    • Simulation period of 1 month
    • Boundary inflows from USGS gauge measurements
  • 4. Data from the SWOT Land Simulator Along-Track Range The SWOT simulator produces data with the correct signal to noise ratio, layover and geometric decorrelation scattering properties. Notice for SWOT the land SNR is low, while surface water stands out.
  • 5. Challenges for near-nadir interferometry over land
    • Topographic layover and low land SNR makes conventional phase unwrapping approaches unfeasible
    • Notice that fringes are well defined over the water, since the water is flat and quite bright at nadir incidence.
    • The signal from topography may contaminate the signal over the water (see next slide)
  • 6. Radar Layover and its Effect on Interferometry Brightness Ratio (land darker than water) Correlation Ratio (land less correlated than water) Volumetric Layover (trees) Surface Layover Points on dashed line arrive at the same time
  • 7. NASA SWOT Land Processing Approach
    • Processing approach relies on having a fair estimate of topography and water body elevation
      • Estimate can be derived from a priori data or previous SWOT passes (to account for dynamics)
    • A priori information is used to generate reference interferograms for phase flattening and estimation of layover (to avoid averaging in land)
  • 8. Interferogram after phase flattening with reference interferogram Noisy interferogram Noisy interferogram after flattening with reference
  • 9. Layover region identification Noisy interferogram Noisy interferogram after flattening with reference
  • 10. Land Processing Flow to Geolocation
  • 11. Layover mask All pixels with any layover are red Mid-Swath Near-Swath
  • 12. What if we accept pixels whose expected error is < 5 cm?
  • 13. From raw heights to hydrologic variables Discharge Width Flow Depth (height from bottom) Slope Manning’s equation
  • 14. Water Classification
  • 15. River Channel Mask . Pavelsky and L. Smith, “Rivwidth: A software tool for the calculation of river widths from remotely sensed imagery,” IEEE Geoscience and Remote Sensing Letters , vol. 5, no. 1, p. 70, 2008.
  • 16. Center Line Mask
  • 17. Get Center Line . Pavelsky and L. Smith, “Rivwidth: A software tool for the calculation of river widths from remotely sensed imagery,” IEEE Geoscience and Remote Sensing Letters , vol. 5, no. 1, p. 70, 2008.
  • 18. Project to Local Coordinates s c
    • Spline interpolate center line to constant separation points downstream
    • Use spline to obtain local tangent plane coordinate system at each point
    • For each point:
      • Use KDTree algorithm to find closest centerline point
      • Project point into local coordinate system to get along and across-track distance
  • 19. Measured Noisy Elevation
  • 20. Unsmoothed Elevation Errors
  • 21. Height Error vs Downstream Distance
  • 22. Height Error vs Downstream Distance with Downstream Averaging
  • 23. Measurement Errors Downstream Averaging: 200 m
  • 24. Measurement Errors Downstream Averaging: 1 km