Time-series InSAR with DESDynI : Lessons from ALOS PALSAR

1. Time-series InSAR with DESDynI:Lessons from ALOS PALSAR Piyush Agrama, Mark Simonsa and Howard Zebkerb aSeismological Laboratory, California Institute of Technology bDepts of EE and Geophysics, Stanford University

2. 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.

3. Overview • Noise levels at L-band vs C-band. • Topography related artifacts • PS-InSAR at L-band • Novel time-series techniques: MInTS.

4. 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.

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

6. 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.

7. L-band vs C-band Temporal correlation Phase noise (mm) C-band L-band L-band 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

8. 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.

9. ALOS and topo-related errors • Due to orbit drift, correlation between Bperp and temporal baseline is 0.7. • DEM error cannot be distinguished easily from deformation features (SBAS). Parkfield, CA

10. 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.

11. Long Valley Caldera • 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. PS pixel mask for ALOS PALSAR C-band image from Hooper et al (2004)

12. 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.

13. 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.

14. Multiscale InSAR Time Series (MInTS) Create Interferograms Unwrapped phase Coherence Time-series products Reconstruct data using inverted coefficients 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)

15. MInTS results at Parkfield Parkfield, CA • Resolution of 200m x 200m. • Same stack of 84 IFGs used for SBAS and MInTS. • Linear velocity and sinusoidal seasonal terms estimated.

16. 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.