Rodriguez_et_al_SWOT_IGARSS2011.ppt

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IGARSSJuly 27, 2011

INTERFEROMETRIC PROCESSING OF FRESH WATER BODIES FOR 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 AreaStudy 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 ModelingHydrodynamic 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

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Data from the SWOT Land SimulatorData from the SWOT Land Simulator

Along-Track

Ran

ge

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

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

Radar Layover and its Effect on Interferometry

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δΦ=arg 1+PLand

PWater

γ Land

γWater

exp i ΦLand − ΦWater( )[ ] ⎡

⎣ ⎢

⎤

⎦ ⎥

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

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NASA SWOT Land Processing Approach

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

Interferogram after phase flattening with reference interferogram

Noisy interferogram Noisy interferogram after flattening with reference

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Layover region identificationLayover region identification

Noisy interferogram Noisy interferogram after flattening with reference

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Land Processing Flow to GeolocationLand Processing Flow to Geolocation

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Layover maskAll pixels with any layover are red

Layover maskAll 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?

What if we accept pixels whose expected error is < 5 cm?

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From raw heights to hydrologic variables

From raw heights to hydrologic variables

Discharge

Width Flow Depth(height from bottom)

Slope

Manning’s equation

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Water ClassificationWater Classification

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River Channel MaskRiver 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 MaskCenter Line Mask

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Get Center LineGet 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 CoordinatesProject to Local Coordinates

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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

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Measured Noisy ElevationMeasured Noisy Elevation

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Unsmoothed Elevation ErrorsUnsmoothed Elevation Errors

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Height Error vs Downstream DistanceHeight Error vs Downstream Distance

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Height Error vs Downstream Distancewith Downstream Averaging

Height Error vs Downstream Distancewith Downstream Averaging

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Measurement ErrorsDownstream Averaging: 200 m

Measurement ErrorsDownstream Averaging: 200 m

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Measurement ErrorsDownstream Averaging: 1 km

Measurement ErrorsDownstream Averaging: 1 km