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Characterizing and estimating noise in InSAR and InSAR time series with MODIS

Geochemistry, Geophysics, Geosystems

By:
,
DOI: 10.1002/ggge.20258

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Abstract

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Characterizing and estimating noise in InSAR and InSAR time series with MODIS
Series title:
Geochemistry, Geophysics, Geosystems
DOI:
10.1002/ggge.20258
Volume
14
Issue:
10
Year Published:
2013
Language:
English
Publisher:
Wiley
Contributing office(s):
Geologic Hazards Science Center
Description:
12 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
4121
Last page:
4132
Number of Pages:
12
Country:
United States
State:
California
Other Geospatial:
Mojave Desert