Assessing Prevalent Open-Source Insar Time Series Analysis Methods for Ground Subsidence Monitoring In Midvaal, South Africa

Ground subsidence is a growing geohazard in the Midvaal region of South Africa, threatening infrastructure, economic activities, and community well-being. Current monitoring techniques often lack the necessary spatial and temporal resolution, highlighting an urgent need for more precise and efficient methods. This study investigates the effectiveness of prevalent open-source Interferometric Synthetic Aperture Radar (InSAR) time series analysis methods and tools for monitoring ground subsidence in Midvaal. We used Sentinel-1 Single Look Complex imagery from January 2019 to December 2021 and evaluated four distinct InSAR workflows: Persistent Scatterer (PS) InSAR using ISCE–StaMPS and SNAP–StaMPS, Small Baseline Subset (SBAS) InSAR using ISCE–StaMPS and HyP3–MintPy. The AW3D, 30m resolution DEM was used for topographic phase correction, and continuous Global Navigation Satellite System (cGNSS) data from Heidelberg (HEID) and Vereeniging (VERG) stations validated InSAR-derived Line-of-Sight (LOS) velocities using metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Range Error. Our results show clear spatial patterns of ground deformation, with ground subsidence concentrated in the northeastern and southeastern regions and uplift in the northwestern region. ISCE-StaMPS PS-InSAR and SBAS-InSAR demonstrated the highest precision with the lowest standard errors, making them suitable for detecting subtle movements. In contrast, HyP3-MintPy SBAS-InSAR, while exhibiting larger standard errors, proved valuable for capturing broader, large-scale deformations, including extreme ground subsidence rates of up to -233.9 mm/year. Correlation analysis revealed a strong positive correlation of 0.7 between ISCE-StaMPS and SNAP-StaMPS PS-InSAR, while other method pairs showed weaker correlations, indicating differences in the type of scatterers, distinct strengths and limitations. Velocity accuracy evaluation against cGNSS data showed that ISCE-StaMPS SBAS-InSAR and SNAP-StaMPS PS-InSAR achieved the lowest MAE and RMSE, meeting the NISAR mission’s validation criterion for secular ground deformation. This research underscores the importance of method selection based on specific study objectives, whether high precision or broad spatial coverage is prioritized, and highlights the need for continued refinement of InSAR techniques for accurate geohazard monitoring in dynamic environments.