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.

Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry

“…The main drawback is that the result can be biased because the unwrapping error is introduced (i.e., by adding a wrong integer ambiguity number 2π during the phase unwrapping). Furthermore, it may be not suitable for studying at local-scale deformations [25,27] and it also has a non-closure phase biased due to working on multilook imagery [28][29][30]. In contrary, in the second approach, the analysis can be carried out using the wrapped phase in model Eq.…”

Spatial Heterogeneity of Uplift Pattern in the Western European Alps Revealed by InSAR Time‐Series Analysis

“…The corresponding maps are computed for 6, 12, or 18-day interferograms. They quantify the bias associated with seasonal vegetation growth, which is introduced by interferogram multi-looking nonlinearity, and which fades with interferogram duration (Ansari et al, 2020). Pixels with large positive or negative bias on the 18-day MLDI map were masked (Figure S1b in Supporting Information S1).…”

Permafrost Dynamics Observatory—Part I: Postprocessing and Calibration Methods of UAVSAR L‐Band InSAR Data for Seasonal Subsidence Estimation

“…While the interferometric phase values approach normality as the resolution is degraded, at the 30 m spatial resolution selected for the data products generated from the PDO joint retrieval, interferometric phase still exhibits a degree of kurtosis, which can complicate ALT estimates when only a single interferogram is available. A recent study demonstrated that the choice of temporal baseline in InSAR time series analysis can similarly introduce interferometric phase biases (Ansari et al, 2020). Much like this counterintuitive result, the choice of spatial resolution may similarly introduce phase biases into both individual interferograms and time series analyses, necessitating further research into interferometric phase biases which are not directly related to geophysical deformation.…”