An End-to-End Python-Based Toolkit for Facilitated Time-Series Interferometric Synthetic Aperture Radar (InSAR) Analysis of Sentinel-1 Remote Sensing Data

Ground deformation (GD) represents a significant geohazard, arising from both natural mechanisms such as tectonic movements and anthropogenic activities, including excessive groundwater extraction. Globally, GD threatens geological stability and civil infrastructure. Consequently, continuous monitoring of GD is vital for characterizing its spatial and temporal behaviour, enabling hazard assessment and improving regional safety. Time-Series Interferometric Synthetic Aperture Radar (TS-InSAR) has emerged as a robust remote sensing approach for long-term GD monitoring. Despite its effectiveness, many existing TS-InSAR processing platforms suffer from notable constraints, including limited geographic flexibility, commercial licensing, and the absence of a comprehensive end-to-end processing framework. Although GMTSAR, one of the most widely used TS-InSAR processing platforms, overcomes some of these shortcomings, it is highly user-driven, remains dependent on manual user input, requires command-line execution via C-shell, lacks a graphical user interface, and does not consider essential processing steps such as interferogram network pruning and unwrapped interferogram anchoring. These limitations reduce usability and may affect processing accuracy. To address these challenges, this study presents DefoEye (v1), an open-source, Python-based toolkit integrated with GMTSAR to enable a complete TS-InSAR processing workflow for Sentinel-1 data through an easy-to-use interface. DefoEye offers a unified end-to-end framework incorporating parallelized processing, interferogram network pruning, and multiple anchoring strategies. Its performance was tested across multiple regions characterized by diverse geological environments, GD drivers, atmospheric conditions, and climatic regimes over varying temporal scales. Validation results show strong agreement between DefoEye-derived GD measurements and independent GNSS observations. Additionally, the results closely match those obtained from other widely used TS-InSAR software packages. Unlike existing processing platforms, which require fragmented workflows across multiple tools, DefoEye streamlines the entire process within a single integrated platform. Overall, the findings demonstrate that DefoEye produces reliable TS-InSAR results applicable to a wide range of geological, hydrological, and environmental studies. The toolkit is publicly available at: https://github.com/ATDehkordi/DefoEye.