SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation monitoring over large areas. This paper examines the spatial characteristics of these errors and introduces a spatially constrained SBAS-InSAR method, termed SSBAS-InSAR, which enhances the accuracy of wide-area surface deformation monitoring. The method employs multiple stable ground points to create a control network that limits the propagation of multiple types of errors in the interferometric unwrapped data, thereby reducing the impact of long-wavelength signals on local deformation measurements. The proposed method was applied to Sentinel-1 data from parts of Jining, China. The results indicate that, compared to the traditional SBAS-InSAR method, the SSBAS-InSAR method significantly reduced phase closure errors, deformation rate standard deviations, and phase residues, improved temporal coherence, and provided a clearer representation of deformation in time-series curves. This is crucial for studying surface deformation trends and patterns and for preventing related disasters.

The Interferometric Synthetic Aperture Radar (InSAR) has been widely used as a powerful technique for monitoring land surface deformations over the last three decades. InSAR observations can be plagued by atmospheric phase delays; some have a roughly linear relationship with the ground elevation, which can be approximated using a linear model. However, the estimation results of this linear relationship are sometimes affected by phase ramps such as orbital errors, tidal loading, etc. In this study, we present a new approach to estimate the transfer function of vertical stratification phase delays and the transfer function of phase ramps. Our method uses the idea of multi-scale spatial differences to decompose the atmospheric phase delay into the vertical stratification component, phase ramp component, and other features. This decomposition makes the correlation between the vertical stratification phase delays and topography more significant and stable. This can establish the correlation between the different scales and phase ramps. We demonstrate our approach using a synthetic test and two real interferograms. In the synthetic test, the transfer functions estimated by our method were closer to the design values than those estimated by the full interferogram–topography correlation approach and the band-pass filtering approach. In the first real interferogram, out of the 9 sub-regions corrected by the proposed method, 7 sub-regions were outperformed the full interferogram–topography correlation approach, and 8 sub-regions were superior to the band-pass filtering method. Our technique offers a greater correction effect and robustness for coseismic deformation signals in the second real interferogram.