December 11, 2009

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Feasibility of retrieving water vapor spatial variations at epochs of SAR acquistions from SAR Interferometry

A case study based feasibility assessment

Fringe2009, Frascati, Italy

Shizhuo Liu, Ramon Hanssen

Delft Institute of Earth Observation and Space Systems

December 11, 2009 2

Revisit frequency for S1A and S1B Days per revisit

Main difficulty:

Data exploration

Spatio-temporal variation between two acquisitions instead of spatial variation at each acquisition

Objective

Retrieving APS spatial variations at epochs of SAR acquisitions from InSAR

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To investigate the feasibility of:

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Algorithm

Step 1. network forming

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In total 33 ASAR acquisitions in descending orbit Introducing baseline constrains

(Bp < 400 m, Bt < 6 months)

Spatio-temporal network 1. remove topographic phase2. unwrap phase3. assume negligible land deformation

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Step 2. parameter estimation

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An example:p: image pixel

Constrained least-squares

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Weighted least-squares estimation (WLSE)

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Stochastic model:Variance component estimation(VCE)

pseudo-observation

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Step 3. Testing

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0213213 ≈=++ eppp ϕϕϕmeasurement with outliers

0*

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( ) yQAAQAxy

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111''bias:

measurement noise (with least-squares adjustment):

DIA:I.Detection: overall residueII.Identification: w-testIII.Adaptation: re-estimation of x after removing outliers

measurement outliers due to e.g. unwrapping error

p21ϕ

p32ϕ

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3t2t

Why?

0312312 =++= ϕϕϕeHow?Noise free:

Step 4. Spatial interpolation and filtering

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Raw result after testing Refined result after kriging

kriging

mm mm

Case study

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Case study

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Result

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Zenith delay spatial variation (zero mean)

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pixel resolution: 1 kmmm

date:

ground size: 80 x 80 km

sorted in terms of delay RMS

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Mean RMS: 3.6 mm

Global climate map

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Result validation

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Mean RMS: 3.6 mm

Validation 1. Bias evaluation

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RMS of the bias:

Validation 2. Power spectral densities

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Regime 2

Regime 1

-5/3, 2D turbulence

-8/3, 3D turbulence

1 km10 km100 km

-8/3

-5/3

Validation 3. Temporal correlation

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Correlation matrix

Main-diagonal: auto-correlation

Off-diagonal: cross-correlation

acquisition index

Mean correlation

0.1

Temporal correlated APS

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20060117 20071113

20080401

20070904

20060606 20070313

MERIS:20060117 MERIS:20071113 MERIS:20060606 MERIS:20070313

mm

North

Validation 4. Compare to MERIS

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mmEstimates:

MERIS:

Summary

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Internal validations

Cross validation with MERIS– good spatial correspondence

- estimation bias should be less than 1 mm (RMS 0.76 mm)

- PSD of estimated APS shows a power-lawbehavior which agrees with the turbulence theory

- low temporal correlation

Conclusion• It is feasible to retrieve APS spatial variation at the

epochs of SAR acquisitions, provided that:

1. Sufficient number of SAR acquisitions (reduce estimation bias)rule of thumb:

2. Short repeat orbit (prevent significant temporal decorrelation)every 6 days for Europe and Canada with Sentinel-1A/B

3. Larger critical baseline (a network with more acquisitions)>> 1100 m for Sentinel-1A/B (4 m range resolution)

4. Negligible or known deformation

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Nbias 1

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