Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes. Coregistration of terrain observation by progressive scans (TOPS) data is a critical step in its application. TOPS data must be fundamentally co-registered with an accuracy of 0.001 pixels. However, various decorrelation factors due to natural vegetation and seasonal effects affect the coregistration accuracy of TOPS data. This paper proposed an enhanced spectral diversity coregistration method for dual-polarimetric (PolESD) Sentinel-1A/B TOPS data. The PolESD method suppresses speckle noise based on a unified non-local framework in dual-pol Synthetic Aperture Radar (SAR), and extracts the phase of the optimal polarization channel from the denoised polarimetric interferometric coherency matrix. Compared with the traditional ESD method developed for single-polarization data, the PolESD method can obtain more accurate coherence and phase and get more pixels for azimuth-offset estimation. In bare areas covered with low vegetation, the number of pixels selected by PolESD is more than the Boxcar method. It can also correct misregistration more effectively and eliminate phase jumps in the burst edge. Therefore, PolESD will help improve the application of TOPS data in low-coherence scenarios.

In the past 30 years, the small baseline subset (SBAS) InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters. Because of its ability to monitor large-scale deformation with millimeter accuracy, the SBAS method has been widely used in various geodetic fields, such as ground subsidence, landslides, and seismic activity. The obtained long-term time-series cumulative deformation is vital for studying the deformation mechanism. This article reviews the algorithms, applications, and challenges of the SBAS method. First, we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm, which provides a basic framework for the following improved time series methods. Second, we classify the current improved SBAS techniques from different perspectives: solving the ill-posed equation, increasing the density of high-coherence points, improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation. Third, we summarize the application of the SBAS method in monitoring ground subsidence, permafrost degradation, glacier movement, volcanic activity, landslides, and seismic activity. Finally, we discuss the difficulties faced by the SBAS method and explore its future development direction.