InSAR data Processing using MintPy in jupyter Notebook

This Jupyter Notebook presents a structured workflow for Interferometric Synthetic Aperture Radar (InSAR) time-series processing using the Miami InSAR Time-series software in Python (MintPy). It provides an edited and modularised implementation of standard MintPy processing scripts, designed to streamline deformation time-series analysis using HyP3-derived SAR data from the ASF portal and a study-area shapefile for geohazard and surface-deformation applications.

The workflow guides users through each critical processing stage, with clear explanations of required steps and parameter choices. Emphasis is placed on reproducibility, modular execution, and transparent parameter control within an interactive Jupyter environment. The notebook is particularly well-suited for researchers and students engaged in landslide monitoring, tectonic deformation analysis, subsidence assessment, and infrastructure stability studies.

This implementation builds upon the core MintPy framework and established InSAR time-series methodologies, with adaptations to support flexible application across different study areas and datasets.

Users are encouraged to adapt and extend the notebook for their own case studies, provided that appropriate citation of MintPy and related methodological literature is maintained.

Core MintPy Reference along with Notebook Citation:

Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331.