InSAR Error Correction and Accuracy Evaluation of Combined GNSS and Leveled Measurement Data

To mitigate the impact of systematic errors—such as orbital errors, Digital Elevation Model (DEM) residuals, and atmospheric delays—on ground settlement monitoring using Interferometric Synthetic Aperture Radar (InSAR), this study proposes a multi-source cooperative correction strategy that integrates Global Navigation Satellite System (GNSS) and leveling data. An error separation model is constructed based on fitting estimation theory, and a two-stage optimization framework, comprising “error separation” and “accuracy assessment,” is developed. This framework quantifies the absolute accuracy and spatial consistency of the deformation field both before and after correction. The leveling dataset serves as an independent validation source. By incorporating GNSS spatial constraints and a priori information on soft soil thickness, the 48-view TerraSAR-X imagery acquired between January 2019 and December 2021 at Xiamen New Airport is utilized to correct systematic inaccuracies dynamically. The experimental results indicate nearly a 40% improvement in both the long-term trend of the deformation field and the spatial consistency of the leveling data after correction. The overall mean error was reduced from 6.4 mm to 4.9 mm, reflecting a 32% increase in accuracy. By reducing the combined effects of orbital errors, Digital Elevation Model (DEM) residuals, and atmospheric delays, the corrected Interferometric Synthetic Aperture Radar (InSAR) data show a stronger positive correlation between soft soil thickness and settlement. These findings confirm that the method effectively decreases systematic errors and boosts monitoring precision, thus providing a solid methodological foundation for millimeter-scale foundation settlement monitoring.