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

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-15
• Main Authors: Zhang, Lele, Han, Wenhui, Jiang, Zhiwei, Kong, Xiaolan, Zeng, Qiming, Xu, Yongxiang, Huang, Pingping
• Format: Article
• Language: English
• Subjects: 3-D deformation; Azimuth; Covariance matrix; Deformation; Delays; Error analysis; error propagation; Error reduction; Extraterrestrial measurements; Interferometric synthetic aperture radar; Measurement uncertainty; Mitigation; multitrack InSAR; Noise reduction; Propagation; Regularization; SAR (radar); stochastic error; Stochastic processes; Stochasticity; Synthetic aperture radar; Synthetic aperture radar interferometry; systematic error; Systematic errors; Systematics
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3392241

Three-dimensional (3-D) deformation could be resolved using multitrack Interferometric Synthetic Aperture Radar (InSAR), with the accuracy dependent on the magnitude of multisource 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 multitrack InSAR measurements in the 3-D direction. The systematic error propagation includes generic systematic error and additional systematic errors (ASEs) 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 multitemporal InSAR (MT-InSAR) techniques. Accordingly, we propose a new 3-D deformation inversion method, combining the covariance matrix and L^2-norm regularization based on multitrack 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.