Time-series InSAR ground deformation monitoring: Atmospheric delay modeling and estimating

• Published in: Earth-science reviews Vol. 192; pp. 258 - 284
• Main Authors: Li, Zhiwei, Cao, Yunmeng, Wei, Jianchao, Duan, Meng, Wu, Lixin, Hou, Jingxing, Zhu, Jianjun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2019
• Subjects: Atmospheric delay; Atmospheric water vapor; deformation; earthquakes; geodesy; geophysics; glaciers; Interferometric Synthetic Aperture Radar (InSAR); interferometry; monitoring; refractive index; spatial variation; Stochastic model; stochastic processes; synthetic aperture radar; time series analysis; Time-series InSAR (TS-InSAR); troposphere; volcanic activity; water vapor; Atmospheric delay; Stochastic model; Interferometric Synthetic Aperture Radar (InSAR); Atmospheric water vapor; Time-series InSAR (TS-InSAR)
• ISSN: 0012-8252; 1872-6828
• DOI: 10.1016/j.earscirev.2019.03.008

Spatial heterogeneity in the atmospheric refractive index causes variations in spaceborne interferometric synthetic aperture radar (InSAR) observations. The neutral atmospheric delay (i.e., the tropospheric delay) on one hand, can introduce large errors in the InSAR-derived displacements associated with earthquakes, volcanic activity, glacier motions, underground resource extraction, and many other crustal deformation phenomena. On the other hand, the tropospheric delay can be used to infer high-resolution maps of the non-differential atmospheric water vapor. Time-series InSAR techniques aim to measure geodetic and/or geophysical parameters of interest by integrating time series of SAR images or interferograms using statistical or adjustment methods, through the reduction of error sources (e.g., atmospheric delays, decorrelation noises). This contribution is intended to systematically review the properties, mitigation, and estimation of the atmospheric delays in TS-InSAR for better monitoring ground deformation. We first review the present TS-InSAR techniques and introduce the spatio-temporal characteristics of the neutral atmospheric delays observed in TS-InSAR. We then present the estimates and integration of atmospheric stochastic models for TS-InSAR applications. We construct, analyze, and illustrate a high-resolution non-differential atmospheric water vapor model from TS-InSAR observations. Finally, we present a discussion and outlook for the TS-InSAR atmospheric delay models and estimation efforts.