Error Propagation and Error Mitigation of Multi-Track InSAR Observations to 3-D Surface Deformation Estimates

Three-dimensional (3-D) deformation could be resolved using multi-track Interferometric Synthetic Aperture Radar (InSAR), with the accuracy dependent on the magnitude of multi-source errors within InSAR measurements. To improve the precision of 3-D deformation, it is essential to understand the error propagation mechanism and then develop the methodology for reducing error impacts in 3-D decomposition processing. In this article, we present an error propagation model that incorporates both systematic and stochastic error propagation, which determines the error contribution of the multi-track InSAR measurements in the 3-D direction. The systematic error propagation includes generic systematic error and additional systematic errors (ASE) in the vertical and east directions caused by neglecting the north component. For stochastic error propagation, we construct the covariance matrix by considering variance and correlation from different InSAR measurements when using differential and multi-temporal InSAR techniques. Accordingly, we propose a new 3-D deformation inversion method, combining the covariance matrix and L2-norm regularization based on multi-track InSAR (CovRM-InSAR) to improve the precision of 3-D deformation with noise reduction. In the case study, we applied Sentinel-1A and ALOS-2 InSAR datasets from four tracks to map 3-D velocity in Wuhai and analyzed the time-series error propagation and 3-D uncertainty. The precision of 3-D deformation resolved by CovRM-InSAR has improved by up to 90%, 44%, and 98% in the vertical, east, and north directions, respectively. Additionally, the CovRM-InSAR has effectively reduced the stochastic errors by up to 38%, 15%, and 90% in the vertical, east, and north directions, respectively.