Time-series InSAR with DESDynI:Lessons from ALOS PALSAR

Piyush Agrama, Mark Simonsa

and Howard Zebkerb

aSeismological Laboratory, California Institute of TechnologybDepts of EE and Geophysics, Stanford University

Motivation• InSAR time-series techniques crucial for many of

DESDynI’s stated objectives - Geohazards, Hydrology and subsurface reservoirs

• Why ALOS PALSAR?– L-band mission similar to DESDynI.– Lifetime similar to DESDynI.

• ALOS PALSAR products – a good proxy for DESDynI products.

Overview

• Noise levels at L-band vs C-band.

• Topography related artifacts

• PS-InSAR at L-band

• Novel time-series techniques: MInTS.

Comparison of Noise Levels

• Typical resolution of interest – 100m x 100m.

• Analysis of filtered interferograms with shortest time span.

• ERS vs ALOS PALSAR.

• Experiments conducted over the San Francisco Bay Area.

L-band 46 day correlation similar to C-band at 1 day

• ERS Looks = 80. ALOS Looks = 336.– Factor of 2 gain.

• Factor of 2 observed in InSAR data.

• L-band Decorrelation at 45 days ~ C-band decorrelation at 1 day

ALOS PALSAR ERS TandemBperp (in m)

Phase noise ( mm)

Bperp (in m)

Phase noise (mm)

410 8.5 275 6.4

98 6.1 113 7.8

159 16.5 94 4.0

932 18.1 - -

102 7.1 - -

39 17.2 - -

Average 12.3 Average 6.1

Areal coverage similar.ALOS coherence threshold = 0.7 .ERS coherence threshold = 0.7.

L-band 46 day correlation 2x C-band at 35 days

• ERS Looks = 80. ALOS Looks = 336.

– Factor of 2 gain.

• Factor of 2 gain in phase noise due to coherence threshold.

• Temporal decorrelation at L-band is significantly lower.

Areal coverage similar.ALOS coherence threshold = 0.7 .ERS coherence threshold = 0.4.

ALOS PALSAR ERS (35 day)Bperp (in m)

Phase noise ( mm)

Bperp (in m)

Phase noise ( mm)

410 8.5 670 15.2

98 6.1 325 11.0159 16.5 498 15.7932 18.1 376 11.5

102 7.1 1010 N/A39 17.2 200 10.5

Average 12.3 Average 12.8

L-band vs C-band

• Decorrelation noise higher at C-band for longer temporal baselines.• Other noise sources - atmosphere etc. are assumed to be on the same order at both C and L bands

Temporal correlation

L-band

C-band

Phase noise (mm)

C-band

L-band

Implications for DESDynI• Lower temporal decorrelation for many interferograms

favors L-band.– More redundant IFG networks for time-series.– More coherent IFGs with longer time spans than C-band

• Reduced temporal decorrelation improves the spatial coverage significantly (for same coherence threshold).

• Improved coherence => Better unwrapping.

• Overall: Comparable sensitivity to C-band time-series InSAR products but with greater spatial coverage for rapid interferograms, much better for longer time spans.

ALOS and topo-related errors

Parkfield, CA• Due to orbit drift, correlation between Bperp and temporal baseline is 0.7.

• DEM error cannot be distinguished easily from deformation features (SBAS).

PS-InSAR at L-band with ALOS• Not as straight-forward as at C-band due to sensor

management.

• ALOS PALSAR – Need to combine different imaging modes.

• Different noise characteristics of FBD and FBS modes.

• Need appropriate weighting of the modes when selecting PS.

• Does work: example over Long Valley Caldera, CA.

Long Valley Caldera

C-band image from Hooper et al (2004)PS pixel mask for ALOS PALSAR

• 23 ALOS PALSAR images with baselines < 4 Km.• PS density is similar to C-band.• Fine tuning needed for handling different modes.• Velocities heavily contaminated by topo-related errors.

Implications for DESDynI• Plan no systematic relationship between temporal and

spatial baselines.

• L-band allows us to implement simple SBAS/ linear inversion approach more reliably due to better coverage.

• Other topo-related errors - like tropospheric delay - at same level as ALOS PALSAR.

• Traditional time-series approaches like SBAS and PS-InSAR should work better for DESDynI than ALOS.

Novel time-series techniques will improve over current methods

• In many situations, deformation estimates at 500m x 500m suffices to model geophysical phenomenon.

• Can exploit the spatially correlated nature of deformation at these spatial scales.

• Can decompose the data into independent components at various spatial scales- e.g, wavelets.

• Multiscale InSAR time-series (MInTS) developed by Hetland and Simons.

Multiscale InSAR Time Series (MInTS)

Create Interferograms Unwrapped phase

Coherence

Create data mask for each IFG and interpolate holes

Compute wavelet coefficients and Weights

for each IFG

Invert wavelet coefficients using temporal model (similar to GPS)

Reconstruct data using inverted coefficients

Time-series products

MInTS results at Parkfield

• Resolution of 200m x 200m.• Same stack of 84 IFGs used for SBAS and MInTS.• Linear velocity and sinusoidal seasonal terms estimated.

Parkfield, CA

Conclusions• Shorter repeat period and acquisitions in a consistent imaging mode over targets make DESDynI superior to ALOS PALSAR.

• Better orbital control and plan significantly decreases uncertainties in deformation estimates due to topo-related errors.

• Uncertainty in deformation time-series will match current C-band products but yield much greater spatial coverage.

• Novel time-series techniques like MInTS can significantly improve deformation estimates over regions where traditional techniques like SBAS and PS fail.