Robust Surface Deformation and Tropospheric Noise Characterization From...

Zebker, M. S., A. Chen, and M. A. Hesse (2023), Robust Surface Deformation and Tropospheric Noise Characterization From Common-Reference Interferogram Subsets, IEEE Trans. Geosci. Remote Sens., 61, 5210914, doi:10.1109/TGRS.2023.3288019.
Abstract: 

Interferometric synthetic aperture radar (InSAR) surface deformation estimates often suffer from tropospheric noise errors, and many study sites do not have global positioning system (GPS) or in situ measurements for validating InSAR surface deformation solutions. Here, we present a method for characterizing both surface deformation and tropospheric noise from interferogram subsets. Choosing different subsets of interferograms that use a common-reference SAR scene allows us to quantify tropospheric noise and deformation signals. We demonstrate this method using 95 C-band Sentinel-1 SAR scenes acquired over the Oman Ophiolite and 133 scenes over the Island of Hawaii. For the Oman case, our method suggests that there is no detectable deformation signal. In this scenario, the average of any subset of interferograms with a common-reference SAR scene estimates the tropospheric noise contribution on that reference SAR date. Achieving ∼0.5 cm of uncertainty requires a subset size of 40 common-reference interferograms. The observed tropospheric noise is non-Gaussian and follows a seasonal pattern. Propagation of tropospheric noise leads to up to ∼5 cm of false deformation signal when deriving either a stacking or Small BAseline Subset (SBAS) time-series solution. For the Hawaii case, our method shows that the observed InSAR phase on the south flank of Kilauea is due to a secular deformation signal, while the phase over Mauna Loa is mostly associated with tropospheric noise. Our results are validated with independent GPS tropospheric zenith delay and surface deformation time series with sub-cm RMS errors.

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Research Program: 
Earth Surface & Interior Program (ESI)
Funding Sources: 
This work was supported in part by the Dr. Floyd F. Sabins, Jr., Summertime Fellowship in Remote Sensing at the University of Texas and in part by the NASA Earth Surface and Interior Program under Grant 80NSSC18K0467.