SWOT - IEEE GRSS

SWOT
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
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Study Area
• ~1000 km reach of the Ohio
River basin
• Drains an area of ~220,000 km2
• 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
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Hydrodynamic Modeling
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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
• Simulation period of 1
month
• Boundary inflows from
USGS gauge
measurements
SWOT Hydrology Virtual Mission
Meeting, Paris, 221-3
Sep 2010
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July 27, 2011
SWOT
Data from the SWOT Land Simulator
Range
Along-Track
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.
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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)
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Radar Layover
and its Effect on Interferometry
Surface Layover
Volumetric Layover (trees)
Points on dashed line arrive at the same time
Correlation Ratio (land less correlated than water)
 PLand  Land

  arg1
exp iLand  W ater
 PW ater  W ater

Brightness Ratio (land darker than water)
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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)
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Interferogram after phase flattening with
reference interferogram
Noisy interferogram
Noisy interferogram after flattening with reference
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Noisy interferogram
Layover region identification
Noisy interferogram after flattening with reference
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Land Processing Flow to Geolocation
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Layover mask
All pixels with any layover are red
Mid-Swath
Near-Swath
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What if we accept pixels whose
expected error is < 5 cm?
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From raw heights
to hydrologic variables
SWOT
Manning’s equation
Discharge
Width Flow Depth
(height from bottom)
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Slope
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Water Classification
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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.
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Center Line Mask
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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.
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Project to Local Coordinates
• Spline interpolate center
line to constant separation
points downstream
• Use spline to obtain local
tangent plane coordinate
system at each point
s
c
• For each point:
-Use KDTree algorithm to find closest
centerline point
- Project point into local coordinate system
to get along and across-track distance
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Measured Noisy Elevation
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Unsmoothed Elevation Errors
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Height Error vs Downstream Distance
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Height Error vs Downstream Distance
with Downstream Averaging
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Measurement Errors
Downstream Averaging: 200 m
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Measurement Errors
Downstream Averaging: 1 km
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