An Algorithm for Estimating and Correcting Decorrelation Phase From InSAR Data Using Closure Phase Triplets

Michaelides, R. J.; Zebker, H. A.; Zheng, Yujie

doi:10.1109/tgrs.2019.2934362

Cited by 28 publications

(20 citation statements)

References 26 publications

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“…Closure phase has also been referred to in literature as "phase consistency" [23] or "phase triangularity" [19]. In contrast to the single-look case, closure phases are generally nonzero for multi-looked observations [23], [25], [26]. We consider pixels with zero closure phase as pixels with "consistent" phase measurements and pixels with nonzero closure phase as pixels with "inconsistent" phase measurements.…”

Section: Technical Background a Closure Phase Definitionmentioning

confidence: 99%

On Closure Phase and Systematic Bias in Multilooked SAR Interferometry

Zheng

Fattahi

Agram

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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In this article, we investigate the link between closure phase and the observed systematic bias in deformation modeling with multi-looked SAR interferometry. Multi-looking or spatial averaging is commonly used to reduce stochastic noise over a neighborhood of distributed scatterers in InSAR measurements. However, multi-looking may break consistency among a triplet of interferometric phases formed from three acquisitions leading to a residual phase error called closure phase. Understanding the cause of closure phase in multi-looked InSAR measurements and the impact of closure phase errors on the performance of InSAR time-series algorithms is crucial for quantifying the uncertainty of ground displacement time-series derived from InSAR measurements. We develop a model that consistently explains both closure phase and systematic bias in multi-looked interferometric measurements. We show that nonzero closure phase can be an indicator of temporally inconsistent physical processes that alter both phase and amplitude of interferometric measurements. We propose a method to estimate the systematic bias in the InSAR time-series with generalized closure phase measurements. We validate our model with a case study in Barstow-Bristol trough, California. We find systematic differences on the order of cm/year between InSAR time-series results using subsets of varying maximum temporal baseline. We show that these biases can be identified and accounted for.

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Section: Technical Background a Closure Phase Definitionmentioning

confidence: 99%

On Closure Phase and Systematic Bias in Multilooked SAR Interferometry

Zheng

Fattahi

Agram

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…(1) where , , for example, is the phase difference for a pixel in the interferogram between epochs i and j, and | | 2 indicates that the result is given modulo 2 (i.e. wrapped) (Michaelides et al 2019;Zwieback et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Nonzero closure phases are a product of the spatial filtering and are mainly associated with the scattering and electrical properties of the ground surface (De Zan et al 2015;Michaelides et al 2019). Previous studies have suggested that changes in soil moisture and in the water content of vegetation might lead to these phase inconsistencies (De Zan and Gomba 2018;De Zan et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Characterizing and Correcting Phase Biases in Short-Term, Multilooked Interferograms

Maghsoudi¹,

Hooper²,

Wright³

et al. 2021

Preprint

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Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth’s surface over large areas and long time periods. A common strategy to overcome coherence loss in long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as “fading signals”) in the interferometric phase, particularly over vegetated areas. We isolate the signature of the phase bias by constructing daisy chain sums of short-term interferograms covering identical 1-year time periods, but using interferograms of different time spans. This shows that the shorter interferograms are more affected by this phenomenon and that different ground cover types are affected differently. We, propose a method for correcting the phase bias, based on the assumption that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach that has been shown to be rather insensitive to the phase bias. Our corrected velocities agree well with those from phase linking approach. Our approach can be applied to global compilations of short-term interferograms and offer the possibility of accurate long-term velocities without a requirement for coherence in long-term interferograms.

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“…First, we generate a series of day-of-year (DOY) pairs for each synthetic interferogram and generate the corresponding unwrapped interferogram by scaling the original unwrapped interferogram by the ratio of the synthetic differential ADDT value to the original ADDT value (see Figure 11). We then add two noise terms to each synthetic interferogram: a white Gaussian noise term, and a decorrelation noise term that is proportional to the Cramer-Rao bound of phase variance (Michaelides, Zebker, & Zheng, 2019). This second term simulates interferometric noise due to signal decorrelation, while the former term simulates thermal noise in the radar instrument.…”

Section: Accepted Articlementioning

confidence: 99%