Cluster-Based Empirical Tropospheric Corrections Applied to InSAR Time Series Analysis

Abstract

Interferometric synthetic aperture radar (InSAR) allows for mapping of crustal deformation on land with high spatial resolution and precision in areas with high signal-to-noise ratios. Efforts to obtain precise displacement time series globally, however, are severely limited by radar path delays within the troposphere. The tropospheric delay is integrated along the full path length between the ground and the satellite, resulting in correlations between the interferometric phase and elevation that can vary dramatically in both space and time. We evaluate the performance of spatially variable, empirical removal of phase-elevation dependence within SAR interferograms through the use of the $K$ -means clustering algorithm. We apply this method to both synthetic test data, as well as to C-band Sentinel-1a/b time series acquired over a large area in south-central Mexico along the Pacific coast and inland—an area with a large elevation gradient that is of particular interest to researchers studying tectonic- and anthropogenic-related deformation. We show that the clustering algorithm is able to identify cases where tropospheric properties vary across topographic divides, reducing total root mean square (rms) by an average of 50%, as opposed to a spatially constant phase-elevation correction, which has insignificant error reduction. Our approach also reduces tropospheric noise while preserving test signals in synthetic examples. Finally, we show the average standard deviation of the residuals from the best-fit linear rate decreases from approximately 3 to 1.5 cm, which corresponds to a change in the error on the best-fit linear rate from 0.94 to 0.63 cm/yr.