Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

Abstract

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

Ice loss in the Northeastern Tibetan Plateau permafrost as seen by 16 yr of ESA SAR missions

“…S7, S8). This confirms the analysis of Andari et al, 2020 [46] showing that short baseline interferograms are the least reliable for deformation retrieval. Biases induced by the integration of short temporal baselines are not only observed in high-moisture regions, but also on the mountain peaks, likely linked to snow and to systematic decorrelations or unwrapping errors induced by the abrupt changes of ground properties.…”

High‐Resolution Surface Velocities and Strain for Anatolia From Sentinel‐1 InSAR and GNSS Data

“…Finally, we estimate the uncertainty in the velocity from its standard deviation using the percentile bootstrap method (Efron & Tibshirani, 1986) ( Figure S4), and we mask pixels based on several noise indices ( Figure S5). We also test for potential velocity biases associated with short temporal baseline interferograms in a Sentinel-1 network (e.g., Ansari et al, 2020) by removing 6-and 12-day pairs for one LiCSAR frame prior to LiCSBAS velocity inversion ( Figure S11); the standard deviation of the difference between these results is small (~2 mm/year).…”

Improving the Resolving Power of InSAR for Earthquakes Using Time Series: A Case Study in Iran

“…Second, topographically correlated atmosphere can map in as we do not include a seasonal term in the fitting. Third, a cumulated bias for linear rate estimation in time series may be expected from the use of short period interferograms (Ansari et al., 2021). Finally, discontinuities from phase unwrapping errors or any observation gap can have a significant impact on linear rate evaluation.…”