Refined InSAR tropospheric delay correction for wide-area landslide identification and monitoring

• Published in: Remote sensing of environment 2022-06, Vol.275, p.113013, Article 113013
• Main Authors: Wang, Yian, Dong, Jie, Zhang, Lu, Zhang, Li, Deng, Shaohui, Zhang, Guike, Liao, Mingsheng, Gong, Jianya
• Format: Article
• Language: English
• Subjects: Alpine regions; Atmospheric correction; Atmospheric models; Deformation; Delay; Elevation; Empirical analysis; Error correction; Heterogeneity; Hydroelectric power; Hydroelectric power stations; Interferometry; Landslides; Landslides & mudslides; Modelling; Monitoring; Multi-temporal moving-window linear model; Parameter estimation; Parameter identification; Parameter sensitivity; Performance evaluation; Phase unwrapping; Regression models; Synthetic aperture radar interferometry; Time series InSAR; Troposphere; Tropospheric delay
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2022.113013

SAR Interferometry (InSAR) proves to be effective for investigating landslides. However, its measurement accuracy is largely limited by the complex atmospheric delay distortion in alpine valley regions, resulting in poor performance of landslides detection and monitoring. Particularly, the spatial atmospheric heterogeneity over wide areas cannot be accurately reflected by conventional empirical phase-elevation models or external data-based methods. In this study, we proposed a multi-temporal moving-window linear model (MMLM) to correct the tropospheric delay for wide-area landslides investigation. This is a linear regression model based on the elevation-phase relationship for modeling multi-temporal phases within a sliding local window. It mitigates the influence of local turbulent phase, local landslide deformation, and phase unwrapping error on parameter estimation, providing precise heterogeneous atmospheric corrections for wide-area InSAR landslide identification and monitoring. A simulation experiment was conducted to analyze the sensitivity of model parameters settings and evaluate the effectiveness of the MMLM model. Furthermore, we demonstrated the performance of the MMLM model through a comparison with the ERA5, GACOS, spatial-temporal filtering, and traditional linear model using descending and ascending Sentinel-1 data over the reservoir area of the Lianghekou hydropower station. Among the above-mentioned methods, the standard deviation of original unwrapped phases achieved the largest decrease of more than 35% and 50% after correction by the MMLM model for the descending and ascending Sentinel-1 tracks, respectively. In addition, the accurate deformation corrected by the MMLM model improved the landslides investigation, not only can help for delineating landslide boundaries in space but also retrieving movement evolution in time. •The MMLM model can estimate laterally varying tropospheric delays over wide area.•The MMLM model restrains the impact of confounding signals on parameter estimation.•The tropospheric delay correction of MMLM outperforms other common methods.•The MMLM model improves wide-area landslides investigation and monitoring.