InSARLite: An Open-Source GUI for Streamlined InSAR Time Series Processing

Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for measuring surface deformation at high spatial resolution over large areas. Despite its demonstrated value across geohazards and Earth-surface processes, broader adoption is often limited by the complexity of end-to-end processing workflows and the technical overhead required to execute them. To reduce this barrier, we present InSARLite, an open-source Python graphical user interface that streamlines Sentinel-1 time-series processing while retaining user control over key processing decisions. InSARLite is designed to cover the complete processing chain, from software installation and project setup to raw SAR data ingestion and deformation time-series products, by wrapping the GMTSAR processing chain and guiding users through a stepwise interface. Rather than fully black-box automation, the workflow is designed to support interactive inspection and parameter selection based on outputs from preceding stages. The software automates routine tasks commonly handled through command-line scripting, including data querying and retrieval, orbit-file acquisition, baseline estimation and network design, interferogram generation, unwrapping, and time-series inversion using Small Baseline Subset approaches. InSARLite also introduces guided decision support features, including master-image selection based on baseline centrality, interactive network inspection and editing, and optional masking through an intuitive interface using mean-correlation thresholds, manual delineation, or a combination of both. Interferograms are unwrapped after defining a correlation threshold and the number of cores while respecting the optional mask if provided, and a user-defined reference point is used to normalize unwrapped interferograms prior to time-series analysis. InSARLite further integrates optional atmospheric correction using the Generic Atmospheric Correction Online Service and supports interactive visualization and export of deformation time series. We demonstrate the capabilities of InSARLite using a fatal rainfall-triggered landslide that occurred on 8 December 2024 in northeastern Türkiye, evaluating whether the failed hillslope exhibited detectable precursory deformation.