Incremental multi temporal InSAR analysis via recursive sequential estimator for long-term landslide deformation monitoring.

Distributed Scatterers Interferometry (DS-InSAR) has been widely applied to increase the number of measurement points (MP) in complex mountainous areas with dense vegetation and complicated topography. However, DS-InSAR method adopts batch processing mode. When new observation data acquired, the entire archived data is reprocessed, completely ignoring the existing results, and not suitable for high-performance processing of operational observation data. The current research focuses on the automation of SAR data acquisition and processing optimization, but the core time series analysis method remains unchanged. In this paper, based on the traditional Sequential Estimator proposed by Ansari in 2017, a Recursive Sequential Estimator with Flexible Batches (RSEFB) is improved to divide the large dataset flexibly without requirements on the number of images in each subset. This method updates and processes the newly acquired SAR data in near real-time, and obtains long-time sequence results without reprocessing the entire data archived, helpful to the early warning of landslide disaster in the future. 132 Sentinel-1 SAR images and 44 TerraSAR-X SAR images were utilized to inverse the line of sight (LOS) surface deformation of Xishancun landslide and Huangnibazi landslide in Li County, Sichuan Province, China. RSEFB method is applied to retrieve time-series displacements from Sentinel-1 and TerraSAR-X datasets, respectively. The comparison with the traditional Sequential Estimator and validation through Global Position System (GPS) monitoring data proved the effectiveness and reliability of the RSEFB method. The research shows that Xishancun landslide is in a state of slow and uneven deformation, and the non-sliding part of Huangnibazi landslide has obvious deformation signal, so continuous monitoring is needed to prevent and mitigate possible catastrophic slope failure events. [ABSTRACT FROM AUTHOR]