A Guide to Open-Source SAR Processing

A Comprehensive Guide to Open-Source SAR Processing Software for Land Subsidence Monitoring in Post-Coal Mining Areas: SBAS and PS-InSAR Applications

Executive Summary

Interferometric Synthetic Aperture Radar (InSAR) has emerged as an indispensable geodetic technique for monitoring surface deformation, offering millimeter-level precision over vast areas, independent of weather conditions. This capability is particularly critical for assessing land subsidence in post-coal mining regions, where underground excavations can lead to significant and often destructive ground movements. This report provides a detailed overview and comparative analysis of prominent free and open-source software packages—ISCE, StaMPS, MintPy, GMTSAR, and PyRate—that support the Small Baseline Subset (SBAS) and Persistent Scatterer (PS-InSAR) procedures essential for such monitoring.
The analysis highlights each software’s capabilities, supported SAR missions, installation requirements, and integration potential within a comprehensive InSAR workflow. While ISCE excels in high-performance interferogram generation, and StaMPS offers advanced PS-InSAR and SBAS algorithms (though with a MATLAB dependency), MintPy and PyRate provide flexible, Python-centric environments for time-series analysis. GMTSAR presents an end-to-end C-based solution with strong GMT integration for visualization. For post-coal mining environments, which are characterized by complex deformation and varying land cover, a hybrid approach combining the strengths of these tools is often optimal. Recommendations emphasize leveraging Sentinel-1 data, considering longer-wavelength SAR for challenging areas, implementing rigorous atmospheric and topographic corrections, and integrating InSAR with other geodetic methods for robust validation. This guide aims to equip PhD researchers with the knowledge necessary to select and effectively utilize open-source InSAR software for their land subsidence monitoring studies.

Website created for this issue in Gemini.

1. Introduction: InSAR for Post-Coal Mining Subsidence

1.1. The Significance of InSAR in Geohazard Monitoring

Interferometric Synthetic Aperture Radar (InSAR) represents a transformative advancement in remote sensing, providing unparalleled capabilities for monitoring ground surface deformation. This active remote sensing technique enables the creation of imagery day or night, regardless of atmospheric conditions such as clouds or haze, and offers millimeter-level precision over wide areas.1 This robust and economical approach stands in stark contrast to traditional ground-based monitoring methods, which are often constrained by high costs, extensive labor requirements, and limited spatial coverage.4
The utility of InSAR extends across a diverse array of geohazard applications. It has proven effective in detecting subtle movements associated with seismic deformation, volcanic activity, and landslides.1 Crucially, InSAR is also a powerful tool for monitoring land subsidence, a phenomenon of particular interest in regions affected by past or ongoing mining operations.1 The ability of InSAR to provide dense spatial and temporal measurements makes it an indispensable tool for comprehensive land deformation studies, especially for dynamic and extensive phenomena like post-mining subsidence. This capacity for wide-area, high-precision monitoring under diverse environmental conditions positions InSAR as a foundational technology for modern geohazard assessment and management.

1.2. Understanding Land Subsidence in Post-Coal Mining Regions

Land subsidence in post-coal mining areas is a significant geohazard resulting from the extraction of underground resources. The voids left behind after coal mining, known as “goafs,” can lead to substantial and often destructive surface deformations, including ground subsidence and the formation of ground fissures.7 These ground movements pose severe threats to surface infrastructure, agricultural lands, and human safety, necessitating continuous and accurate monitoring.7
InSAR has emerged as a particularly valuable and effective tool for monitoring mining-induced subsidence, especially over these goaf areas, due to its ability to capture intricate spatiotemporal variations in surface deformation.9 Studies have demonstrated InSAR’s capacity to detect a wide range of subsidence rates in mining environments, from millimeters per year to several meters annually. For instance, observed subsidence rates can exceed 15 cm per year, with cumulative deformations reaching 170 cm in some open-pit mining areas.12 Other studies report maximum Line-of-Sight (LOS) subsidence rates of -65 mm/year with cumulative values of -246 mm 13, or even up to 1.26 meters over a two-year period in specific coal mining regions.14 In gold mining areas, deformation rates of -90 mm/year have been detected.15
However, the dynamic and often non-linear nature of post-coal mining subsidence, coupled with potentially large deformation gradients and changes in surface characteristics (such as vegetation cover or disturbed ground), presents notable challenges for InSAR processing. These conditions can lead to unwrapping phase errors and increased decorrelation in SAR interferograms.12 Such decorrelation, characterized by the loss of coherence in SAR signals, is particularly common in non-urban coal mining areas due to variations in land cover and pronounced deformation gradients.14 Therefore, the selection of robust InSAR processing software capable of mitigating these specific issues is essential for generating accurate and reliable deformation measurements in these complex environments.

1.3. Overview of Multi-Temporal InSAR: PS-InSAR and SBAS Techniques

Multi-Temporal InSAR (MT-InSAR) techniques, including Persistent Scatterer InSAR (PS-InSAR) and Small Baseline Subset (SBAS), represent advanced methodologies developed to overcome the inherent limitations of traditional Differential InSAR (D-InSAR). These limitations often include issues related to spatiotemporal decorrelation and atmospheric artifacts, which can obscure the true deformation signal. MT-InSAR methods significantly enhance the accuracy and reliability of deformation monitoring over extended periods.1
Persistent Scatterer InSAR (PS-InSAR) is a technique optimized for analyzing deformation from isolated, highly coherent pixels, known as Persistent Scatterers (PS). These stable targets typically correspond to artificial structures such as buildings or exposed bedrock, which maintain consistent radar reflectivity over time.16 PS-InSAR focuses on identifying these stable points where the noise phase is sufficiently small to avoid obscuring the deformation signals.16 The Stanford Method for Persistent Scatterers (StaMPS) is a notable software package that employs a spatial correlation approach for PS identification, allowing it to detect a higher density of PS points even in rural or less urbanized areas.16
In contrast, the Small Baseline Subset (SBAS) technique is optimized for distributed pixels and aims to provide more continuous spatial coverage where phase unwrapping is successful.16 SBAS operates by combining interferograms with small spatial and temporal baselines, a strategy designed to mitigate decorrelation and enhance both temporal and spatial sampling of the deformation field.4 This approach is particularly advantageous in areas with distributed scatterers, such as natural landscapes or disturbed ground, which are characteristic of post-mining environments.
The choice between PS-InSAR and SBAS, or a hybrid approach, is critical for accurately monitoring land subsidence in post-coal mining areas. While PS-InSAR may be suitable for monitoring stable infrastructure within or adjacent to mining sites, SBAS’s ability to handle distributed scatterers and provide broader coverage is often more advantageous for the heterogeneous, disturbed, or vegetated landscapes typical of post-mining environments, which frequently exhibit complex and large-scale deformation. Software packages like StaMPS and MintPy offer capabilities for both PS-InSAR and SBAS, or even combined multi-temporal (CMT) approaches, providing the flexibility needed to adapt to the specific characteristics of the deformation and land cover.16 This adaptability is crucial for maximizing the density and reliability of measurement points across diverse mining landscapes.

2. Core Open-Source SAR Processing Software for InSAR

2.1. ISCE (InSAR Scientific Computing Environment)

The InSAR Scientific Computing Environment (ISCE) is a robust, open-source, and modular software framework developed by NASA’s Jet Propulsion Laboratory (JPL) to address the needs of the geophysical research community for InSAR data processing and stack generation.6 It is specifically designed for the efficient and systematic processing of large quantities of SAR data, enabling comprehensive time-series analysis.6 ISCE significantly enhances processing accuracy, reducing geolocation errors from several hundred meters (common in older software like ROI_PAC) to sub-pixel levels, which greatly facilitates the automation of processing numerous datasets within a common coordinate system.6 Furthermore, ISCE boasts impressive processing speeds, capable of operating up to 10 times faster than its predecessors.6 Its modular architecture allows for the easy integration of new processing modules, ensuring its adaptability for future InSAR datasets, including airborne and spaceborne missions from both U.S. and international sources.6 ISCE is also designed to cater to users of all skill levels, offering predefined “recipes” for novices while allowing expert users to adapt the software for more advanced functions.6
ISCE supports a wide array of current and past SAR missions. It is frequently employed for processing Sentinel-1 data 22 and can handle data from ALOS, Radarsat-2, and SAOCOM missions.23 The software’s development was influenced by the requirements of future missions like DESDynI, ensuring its forward compatibility.6
Installation of ISCE typically occurs on Linux platforms.19 The software is a hybrid of Python, C, C++, and Fortran routines, with Python serving as an accessible interface for developing intricate workflows.25 It is available for download under a free-for-research agreement, often distributed through consortia like UNAVCO.6
As an actively developed project, ISCE has ongoing issues and improvements. Recent reports on its GitHub repository indicate challenges with ALOS/ALOS2 data interferogram generation, Sentinel-1C processing, Radarsat-2 data, and various errors related to unwrapping, geolocation, and merging steps.24 These issues are actively tracked and addressed by the development community.
ISCE serves as a powerful, high-performance engine for the initial stages of InSAR processing, particularly for generating interferograms and preparing data stacks. Its emphasis on accuracy and speed makes it highly suitable for handling the large volumes of SAR data often required for multi-temporal analysis in PhD research. However, its command-line interface and mixed-language codebase may present a steeper learning curve compared to more GUI-based tools. Researchers should be prepared to engage with its active development community for troubleshooting and to leverage available documentation and tutorials.25

2.2. StaMPS (Stanford Method for Persistent Scatterers)

The Stanford Method for Persistent Scatterers (StaMPS) is a widely recognized and powerful software package for advanced multi-temporal InSAR analysis. It implements Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset (SBAS) InSAR, and Combined Multi-Temporal (CMT) InSAR techniques.20 A key innovation of StaMPS is its use of spatial correlation for PS identification, a feature that allows it to overcome limitations of earlier PS-InSAR methods and identify a higher density of PS points, even in challenging environments like rural regions.16 This capability is particularly beneficial for monitoring deformation in areas with mixed land cover, such as post-coal mining sites. StaMPS also incorporates a sophisticated 3D phase unwrapping algorithm, which is crucial for reliable deformation estimation over time.16 For SBAS analysis, StaMPS generates differential interferograms from multi-temporal radar images and identifies Slowly Decorrelating Filtered Phase (SDFP) pixels, which are highly coherent points used for deriving deformation magnitudes and velocities.20
StaMPS is designed to work with interferograms generated by various conventional InSAR processors, including SNAP, GMTSAR, ISCE, DORIS, and GAMMA.16 This interoperability allows researchers to integrate StaMPS into diverse processing workflows. A common practice involves using SNAP for initial SAR data processing and interferogram generation, followed by exporting the differential interferograms to StaMPS for advanced PS identification, selection, and displacement estimation.1 Similarly, toolboxes like EZ-InSAR streamline this process by using ISCE for interferogram generation and then passing the data to StaMPS for time-series analysis.19 StaMPS has been successfully applied using data from a variety of sensors, including ENVISAT ASAR 20 and Sentinel-1, often after pre-processing with other tools.10
StaMPS is primarily based on MATLAB scripts, with some C code for data conversion.16 Tools that integrate StaMPS, such as EZ-InSAR, typically require a commercial MATLAB license (version 2020b or later) and are developed on a Linux platform.19 Additional dependencies, such as the TRAIN package for tropospheric error correction, may also be necessary to achieve optimal results.19
While StaMPS offers highly sophisticated algorithms for multi-temporal InSAR analysis, particularly excelling in PS identification and phase unwrapping, its reliance on a commercial software like MATLAB presents a potential barrier for users strictly adhering to “free software” requirements. The documentation for StaMPS has historically been scattered across various manuals, tutorials, and forum posts, leading to a steep learning curve for new users.31 However, it is an actively developed project, with ongoing efforts to improve documentation and introduce new features. Forthcoming versions are planned to include automated algorithms for fixing unwrapping errors and parallelization, alongside continued work on ISCE support.32 This strong integration with other pre-processing tools underscores the common practice of building multi-software workflows for comprehensive InSAR analysis.

2.3. MintPy (Miami INsar Time-series software in PYthon)

MintPy (Miami INsar Time-series software in PYthon) is a versatile open-source package designed for Interferometric Synthetic Aperture Radar (InSAR) time-series analysis, primarily implemented in Python.33 It is capable of reading stacks of coregistered and unwrapped interferograms from various sources and formats, and subsequently produces three-dimensional (2D in space and 1D in time) ground surface displacement data in the line-of-sight direction.33 MintPy supports both SBAS and PS-InSAR approaches, making it a flexible tool for diverse deformation studies.19
The software includes a routine time-series analysis workflow, accessible via smallbaselineApp.py, which automates several crucial steps. This workflow processes unwrapped interferograms, references them to a common coherent pixel, calculates phase closure, estimates unwrapping errors, and inverts the interferogram network into time-series displacement data.33 MintPy also applies various essential corrections to refine the displacement signal. These include corrections for local oscillator drift (for specific sensor data like Envisat), stratified tropospheric delay (utilizing global atmospheric models or a phase-elevation-ratio approach), phase ramps, and Digital Elevation Model (DEM) errors, ultimately leading to the estimation of ground velocity.33
MintPy demonstrates high flexibility in data compatibility, capable of ingesting interferogram stacks from a wide array of popular InSAR processors, such as ISCE, ARIA, FRInGE, HyP3, GMTSAR, SNAP, GAMMA, and ROI_PAC.33 This broad compatibility allows it to integrate seamlessly into diverse processing chains, making it a central hub for multi-software workflows. It is particularly consistent with ISCE-processed interferograms 35 and supports Sentinel-1 data, a common and freely available source for subsidence monitoring.35 A testament to its robustness and future relevance, MintPy was selected by the NISAR mission for its requirement validation efforts.18
As a Python-based tool, MintPy offers significant flexibility for customization. Its modular design, featuring individual utility scripts and well-commented code, empowers users familiar with Python to build customized processing recipes and develop their own functions and modules.33 Comprehensive tutorials, often provided in Jupyter Notebook format, further facilitate learning and reproducibility.33
MintPy is under active development, with ongoing bug fixes and feature requests managed through its GitHub issues and Google Groups forum.39 Recent issues reported include problems with specific file generations (e.g.,
unwrapPhase_phaseClosure), geocoding errors, and broken documentation links.40
MintPy stands out as a highly adaptable and user-friendly solution for InSAR time-series analysis, especially for researchers who prefer a Python environment. Its ability to ingest data from almost any major InSAR pre-processor positions it as a central component in complex processing pipelines. The active development and strong community support, coupled with its selection for the NISAR mission, solidify MintPy as a forward-looking and reliable choice for PhD-level research in geohazards.

2.4. GMTSAR (Generic Mapping Tools SAR)

GMTSAR (Generic Mapping Tools SAR) is an open-source InSAR processing system primarily written in C, designed for users already familiar with Generic Mapping Tools (GMT).2 This software is fully capable of processing Small Baseline Subset (SBAS) time-series data, making it a strong contender for land subsidence monitoring.4 GMTSAR supports SAR products from a wide array of missions, including Envisat, ALOS-1, TerraSAR-X, COSMOS-SkyMed, Sentinel-1 (with full TOPS-mode support), and ALOS-2.4
A significant advantage of GMTSAR is its deep integration with GMT, a powerful suite of tools for manipulating and displaying geospatial data. This integration allows for robust post-processing capabilities, including filtering interferograms and constructing interferometric products (such as phase, coherence, and displacement maps) in both radar and geographic coordinates. GMT is then utilized to display all processed products as PDF files and KML images for visualization in Google Earth.42 This tight coupling provides superior visualization capabilities for scientific reporting.
Installation of GMTSAR typically requires a Linux environment, as it is written in C and depends on GMT and NETCDF libraries.42 A detailed, step-by-step installation guide for Ubuntu 20.04 is available, covering all necessary dependencies and configuration steps, including the installation of SNAPHU for phase unwrapping.4
As an open-source project, GMTSAR encourages user contributions.42 While it offers high processing performance, particularly due to its C-based core, direct parallel processing schemes are not fully implemented within GMTSAR itself. This presents opportunities for performance optimization, especially for computationally intensive steps like cross-correlation, where ongoing work includes the development of GPU parallel algorithms.44 Sentinel-1 processing capabilities have been actively developed, with preliminary time-series scripts already made available.32
GMTSAR provides a comprehensive, end-to-end open-source solution for InSAR processing, with a strong emphasis on SBAS. Its C-based core suggests high computational efficiency, which is beneficial for handling large datasets. The tight integration with GMT offers superior visualization capabilities for scientific reporting. However, users should be prepared for a command-line interface and potential manual optimization for parallel processing, which might require a solid understanding of Linux environments and scripting. The project’s active development and community support, accessible through its documentation website and wiki, are valuable resources for researchers.42

2.5. PyRate

PyRate is a Python-based tool specifically designed for estimating the average displacement rate (velocity) and cumulative displacement time-series of surface movements from Interferometric Synthetic Aperture Radar (InSAR) data.48 It exclusively employs a “Small Baseline Subset” (SBAS) processing strategy, making it a focused solution for this particular multi-temporal InSAR approach.48 PyRate is compatible with input data generated by other InSAR processors, specifically supporting GAMMA or ROI_PAC software formats.48 A notable integration feature is the “PyRate export” capability within the European Space Agency’s SNAP software (version 8), which prepares SNAP output data in the GAMMA format for direct use with PyRate.48 This streamlines the pre-processing chain, particularly for widely used Sentinel-1 data.
The PyRate workflow is structured into several sequential processing steps, which can be executed individually or as a complete pipeline: conv2tif (converts interferograms to GeoTIFF format), prepifg (performs multilooking, cropping, and coherence masking), correct (calculates and applies phase corrections), timeseries (performs time-series inversion), stack (stacks the interferogram phase data), and merge (reassembles computed tiles and saves them as GeoTIFFs).48 PyRate and its Python dependencies can be easily installed directly from the Python Package Index (PyPI) using
pip.48 It supports Python 3.7 and 3.8.49
PyRate supports a broad range of SAR missions indirectly through its compatibility with various input formats. These include, but are not limited to, ALOS 1&2, Capella, ENVISAT, ERS 1&2, Gaofen-3, Iceye, Kompsat-5, Paz, NovaSAR, Radarsat 1&2, RCM, Risat-1, SAOCOM, Seasat, Sentinel-1, Spacety, StriX, TerraSAR-X/TanDem-X, and UAVSAR, provided their data can be pre-processed into GAMMA or ROI_PAC formats.50
Based on the provided information, there are no specific active development issues or bug fixes directly related to PyRate’s InSAR processing capabilities. Some snippets mentioning “Pyrate” refer to unrelated topics such as pesticides or video game updates, which are not relevant to its InSAR functionality.51 The apparent stability, indicated by the lack of reported InSAR-specific issues, suggests a mature tool, though users should always consult its official GitHub repository for the latest status and community discussions.48
PyRate offers a streamlined, Python-centric approach specifically for SBAS time-series analysis, making it a strong choice for users who prioritize ease of installation and a clear workflow within a Python environment. Its direct export compatibility with SNAP simplifies the pre-processing chain for Sentinel-1 data, which is a common and freely available source for subsidence monitoring.

3. Comparative Analysis and Workflow Integration

3.1. Feature Comparison: PS-InSAR vs. SBAS Implementation Across Software

The selection between PS-InSAR and SBAS techniques is fundamentally guided by the characteristics of the study area and the nature of the deformation. PS-InSAR, as implemented in software like StaMPS, is particularly effective at identifying and analyzing deformation from isolated, highly coherent scatterers. These are typically found in urban environments with stable structures or in areas with prominent, unchanging natural features.16 StaMPS is noted for its ability to identify a higher density of PS points even in rural or mixed regions, expanding its applicability beyond strictly urban settings.16
Conversely, SBAS, employed by tools such as GMTSAR, MintPy, and PyRate, is designed for analyzing deformation across distributed scatterers. This approach aims to provide more continuous spatial coverage, which is often more suitable for natural or disturbed landscapes, such as those found in post-mining areas.4 A key advantage of SBAS is its enhanced ability to overcome decorrelation in regions experiencing high distortion rates, a common challenge in dynamic mining environments.11
Several software packages, including StaMPS and MintPy, offer capabilities for both PS-InSAR and SBAS, or even combined multi-temporal (CMT) approaches.16 This dual or hybrid capability provides significant flexibility in analysis, allowing researchers to adapt their methodology based on the specific characteristics of the deformation and the varying land cover within their study area. For monitoring land subsidence in post-coal mining areas, which often feature a complex mix of stable infrastructure, disturbed ground, and diverse vegetation, relying on a single InSAR technique may not be sufficient. Therefore, software that supports both PS-InSAR and SBAS, or facilitates a hybrid approach, offers the most comprehensive analytical capability. This flexibility is crucial for adapting to the heterogeneous scattering characteristics of mining landscapes and for maximizing the density and reliability of measurement points across the entire study region.

3.2. Data Compatibility and Supported SAR Missions

The diverse landscape of open-source InSAR software offers broad compatibility with various SAR missions and data formats, which is a significant advantage for researchers. The following table provides a comparative overview of the core software discussed, highlighting their primary functions, supported InSAR techniques, main programming languages, key supported SAR missions, and essential dependencies.
Table 1: Comparison of Open-Source InSAR Software Capabilities

Software Name
Primary Function
Supported InSAR Techniques
Main Programming Language(s)
Key Supported SAR Missions
Key Dependencies/Environment
ISCE
Interferogram Formation, Stack Generation
D-InSAR, Time-series prep
Python, C, C++, Fortran
Sentinel-1, ALOS, Radarsat-2, SAOCOM, DESDynI
Linux, UNAVCO
StaMPS
Time-series Analysis
PS-InSAR, SBAS, CMT-InSAR
MATLAB, C
ENVISAT, Sentinel-1 (via pre-processors)
MATLAB, TRAIN
MintPy
Time-series Analysis
SBAS, PS-InSAR
Python
ISCE, ARIA, FRInGE, HyP3, GMTSAR, SNAP, GAMMA, ROI_PAC formats, Sentinel-1, NISAR
Python libraries
GMTSAR
Full InSAR Processing (prep, proc, post-proc)
SBAS, D-InSAR, Time-series
C
Envisat, ALOS-1/2, TerraSAR-X, COSMOS-SkyMed, Sentinel-1, Radarsat-2
GMT, NETCDF, SNAPHU
PyRate
Time-series Analysis
SBAS
Python
ALOS 1&2, Capella, ENVISAT, ERS 1&2, Gaofen-3, Iceye, Kompsat-5, Paz, NovaSAR, Radarsat 1&2, RCM, Risat-1, SAOCOM, Seasat, Sentinel-1, Spacety, StriX, TerraSAR-X/TanDem-X, UAVSAR (via pre-processors)
Python libraries

The widespread support for Sentinel-1 data across almost all recommended open-source software is a critical advantage for PhD researchers. Sentinel-1 offers free, readily available C-band SAR imagery with high temporal resolution, which significantly reduces data acquisition barriers and enables consistent long-term monitoring of land subsidence. The ability of time-series analysis tools such as MintPy, PyRate, and StaMPS to ingest data from various pre-processors (e.g., ISCE, SNAP, GMTSAR) creates a highly flexible ecosystem. This flexibility allows researchers to combine the strengths of different tools in a customized workflow, optimizing for specific data types, computational resources, and the unique deformation characteristics of their study area.

3.3. Ease of Use, Learning Curve, and Computational Requirements

While all recommended software are open-source and thus “free” in terms of licensing, the actual “cost” often shifts to computational resources and the user’s investment in learning and mastering the tools.
Ease of Use & Learning Curve: Some tools are designed with user-friendliness in mind. For instance, EZ-InSAR provides a graphical user interface (GUI) to minimize user effort, making it accessible even for those less familiar with InSAR processing.19 Similarly, ISCE offers “predefined recipes” to simplify common applications for novice users.6 MintPy, being Python-based, provides routine workflows (e.g.,
smallbaselineApp.py) and extensive tutorials, often in Jupyter Notebook format, which contribute to a relatively accessible learning curve for users comfortable with Python scripting.33 In contrast, StaMPS is known to have a steeper learning curve, partly due to its documentation being scattered across various manuals and forum posts.31 GMTSAR is designed for users already familiar with Generic Mapping Tools (GMT), implying a prerequisite knowledge of GMT’s command-line interface and scripting environment.42
Computational Requirements: Processing large volumes of SAR data for time-series analysis is inherently computationally intensive. ISCE is highly flexible and can run on various platforms, including massively parallel supercomputers, and is specifically designed for the efficient processing of large datasets.6 GMTSAR also offers high processing performance, and there is ongoing work to enhance its parallel implementation, including GPU parallel algorithms for computationally demanding steps like cross-correlation.44 PyGMTSAR, a related project, emphasizes its ability to handle thousands of datasets efficiently even on standard commodity hardware or via cloud services like Google Colab, which can significantly reduce local computational demands.21
Python-based tools (MintPy, PyRate) generally offer a more accessible entry point and a flexible scripting environment, appealing to a broader research audience due to Python’s widespread use in scientific computing. However, C/MATLAB-based tools (GMTSAR, StaMPS) may offer performance advantages for very large datasets or highly optimized algorithms, albeit potentially requiring more specialized system configurations or a deeper understanding of low-level programming. Researchers should carefully balance the learning curve and their existing technical proficiency with the specific computational demands of their study area and data volume when selecting software.

3.4. Community Support, Documentation, and Development Status

The vibrant and collaborative nature of the open-source InSAR community is a significant asset for PhD researchers. This collective environment provides invaluable resources for troubleshooting, learning advanced techniques, and staying updated on the latest developments, thereby mitigating some of the challenges associated with using “free” software.
Documentation & Tutorials: All recommended software projects offer some form of documentation and tutorials. SNAP provides comprehensive tutorials on its ESA STEP page and maintains a community forum for user queries.59 While StaMPS documentation can be scattered, community efforts exist to compile more comprehensive guides and address common issues.31 MintPy boasts extensive documentation, including practical Jupyter Notebook tutorials, and actively encourages community contributions to its codebase and documentation.38 ISCE provides detailed documentation and tutorials through its GitHub repositories and via specialized workshops organized by UNAVCO.25 GMTSAR maintains its own documentation website and a wiki, offering resources for users.42 PyRate also offers documentation on its website and practical tutorials on GitHub.48
User Forums/Community: Active user communities are vital for the sustained use and development of open-source software. SNAP, StaMPS, MintPy, ISCE, and GMTSAR all benefit from active forums (e.g., ESA STEP forum for SNAP, Google Groups for MintPy) or well-maintained GitHub issue trackers. These platforms serve as crucial hubs where users can seek help, report bugs, discuss development, and share best practices.24
Development Status: All listed software projects demonstrate active and ongoing development, characterized by regular updates, bug fixes, and the implementation of new features. This continuous improvement is evident from their respective release notes, GitHub issue trackers, and developer announcements.24 This active development ensures that the tools remain relevant, address emerging challenges, and incorporate the latest advancements in InSAR processing. The presence of a strong, collaborative community fosters a self-sufficient and adaptable research environment, which is highly beneficial for the long-term success of a PhD project.

3.5. Recommended Integrated Workflows for Mining Subsidence

The modular nature of open-source InSAR software enables the construction of highly customized and robust processing workflows by combining the strengths of different tools. This “best-of-breed” approach allows researchers to select the most efficient and accurate components for each stage of the InSAR pipeline, optimizing for factors such as data type, computational resources, and the specific deformation characteristics of post-mining areas. The following table illustrates common integrated InSAR processing workflows, providing concrete, actionable strategies for land subsidence monitoring.
Table 2: Common Integrated InSAR Processing Workflows
Workflow Name/Purpose
Software for Pre-processing/Interferogram Generation
Software for Time-series Analysis (SBAS/PS-InSAR)
Key Data Flow/Integration Method
Rationale/Benefit
SNAP-StaMPS Workflow
SNAP
StaMPS
Export differential interferograms from SNAP in GAMMA format, then transfer to StaMPS for PS/SBAS processing.
Leverages SNAP’s user-friendly GUI for initial steps and StaMPS’s advanced algorithms for time-series analysis, particularly strong in PS identification. 1
ISCE-StaMPS Workflow
ISCE
StaMPS
ISCE generates interferograms and data stacks, which StaMPS then uses for PS/SBAS processing.
Combines ISCE’s high-performance interferogram generation with StaMPS’s specialized time-series capabilities. 19
ISCE-MintPy Workflow
ISCE
MintPy
ISCE generates robust data stacks (e.g., in ISCE format), which MintPy then uses for flexible Python-based SBAS/PS time-series analysis and various corrections.
Ideal for Python-centric users, combining ISCE’s efficiency with MintPy’s analytical power and adaptability. 33
SNAP-PyRate Workflow
SNAP
PyRate
SNAP processes SAR data and uses its “PyRate export” capability to prepare output in GAMMA format for PyRate’s SBAS processing.
Provides a streamlined Python-based SBAS workflow starting from SNAP-processed data, capitalizing on SNAP’s wide mission support. 48
GMTSAR End-to-End SBAS
GMTSAR
GMTSAR
GMTSAR handles the full processing chain, from raw data pre-processing to SBAS time-series analysis and visualization.
Offers a comprehensive solution within a single system, particularly beneficial for users already proficient with GMT for visualization and plotting. 4

This modular approach allows researchers to select the most suitable tools for each stage of their InSAR analysis, adapting to specific data types, computational resources, and the unique characteristics of the deformation being studied in post-mining areas.

4. Practical Considerations for Post-Coal Mining Environments

4.1. Addressing Large Deformation and Decorrelation Challenges

Post-coal mining environments are characterized by complex and often severe ground deformation, making them particularly challenging for InSAR monitoring. The primary technical hurdles involve maintaining coherence and accurately unwrapping phase in areas experiencing large and non-linear movements.
Large Deformation: Land subsidence in mining areas can be rapid and substantial, frequently occurring at submeter to meter scales over short periods.12 Such large deformation gradients pose significant challenges to InSAR processing, primarily leading to unwrapping phase errors and increased decorrelation.12 While sub-band InSAR techniques theoretically aim to reduce phase gradients for large-scale deformations, their practical application is limited by the bandwidths of current SAR satellites.1 For areas experiencing very large and rapid movements (e.g., meters of subsidence), amplitude tracking (also known as pixel tracking) might be necessary to complement or even replace traditional interferometric methods, as InSAR’s phase-based measurements can saturate.78
Decorrelation: Decorrelation, which is the loss of coherence in SAR signals, is a pervasive issue in coal mining areas. This phenomenon is exacerbated by dynamic land cover changes (e.g., agricultural activities, vegetation growth/removal) and the presence of large deformation gradients.14 Decorrelation can significantly limit the density of measurement points obtainable through PS-InSAR techniques, especially in vegetated or disturbed regions.79 SBAS techniques are generally more robust in overcoming decorrelation in regions with high distortion rates compared to traditional PS-InSAR, as they combine multiple interferograms with small baselines to maintain coherence.11 Advanced Multi-Temporal InSAR (MT-InSAR) techniques are specifically designed to reduce spatiotemporal decorrelation effects.1 For example, StaMPS utilizes spatial correlation for PS identification, which enables the extraction of more measurement points even in rural or mixed environments.16 MintPy’s temporal coherence metric serves as a key indicator for assessing the reliability of SBAS network inversions, helping to identify and filter unreliable results.80
A robust approach for a PhD thesis investigating post-coal mining subsidence would likely involve several strategies to mitigate these challenges. First, prioritizing SAR data from longer wavelength missions, such as L-band data from ALOS-2, is beneficial, as longer wavelengths can penetrate vegetation more effectively and are less susceptible to decorrelation over disturbed ground.3 Second, employing advanced SBAS or combined MT-InSAR techniques that are inherently more resilient to decorrelation is crucial. Finally, integrating InSAR results with other geodetic methods, such as GNSS, precise leveling, or Unmanned Aerial Vehicle (UAV) photogrammetry, can provide comprehensive validation and capture different scales or types of deformation, especially in areas of large or complex movement where InSAR alone might be insufficient.81

4.2. Importance of Ancillary Data (DEM, Tropospheric Corrections)

The accuracy of InSAR-derived deformation maps, particularly for subtle movements like those observed in post-mining subsidence, is highly dependent on the effective mitigation of noise sources. Two critical ancillary data types for this purpose are Digital Elevation Models (DEMs) and information for tropospheric corrections.
Digital Elevation Model (DEM): An accurate external DEM is fundamental for correctly removing the topographic phase component from interferograms.20 This step is crucial because the interferometric phase contains contributions from both ground deformation and topography. Software like ISCE extensively utilizes DEMs for precise height correction and to simulate the topographic phase, which is essential for accurate coregistration and interferogram formation.22 Inaccurate DEMs can introduce significant errors into the final deformation product, potentially masking true signals or creating spurious deformation patterns.
Tropospheric Corrections: Atmospheric propagation delays represent a significant source of error in InSAR measurements. These delays, caused by variations in atmospheric pressure, temperature, and humidity, can often exceed the ground deformation signal of interest, particularly for small-magnitude movements.83 Therefore, applying robust tropospheric corrections is vital for isolating the true deformation signal from atmospheric noise. Various software packages offer modules for these corrections: StaMPS can integrate the TRAIN package for atmospheric phase screen correction 19, MintPy can utilize PyAPS or leverage global atmospheric models for similar work 19, and GMTSAR can apply Generic Atmospheric Correction Online Service for InSAR (GACOS) data.45 The quality of the ancillary data used for these corrections and the careful application of appropriate correction models are paramount for achieving reliable and scientifically defensible results in a PhD thesis.
While open-source software provides the necessary tools for these corrections, researchers must exercise diligence in selecting high-quality DEMs and atmospheric models. The careful implementation of these corrections is a critical step in ensuring the precision and reliability of InSAR deformation products, especially when monitoring complex and potentially subtle ground movements in post-mining areas.

4.3. Case Studies of InSAR Application in Mining Subsidence

Numerous studies have demonstrated the proven applicability of InSAR, and specifically the open-source tools discussed in this report, for monitoring diverse types of land subsidence in mining environments. These empirical examples validate the proposed methodologies for PhD research and provide concrete illustrations of expected outcomes and challenges. The following table summarizes selected case studies, highlighting the SAR data, InSAR techniques, and software used, along with key findings.
Table 3: Selected Case Studies of InSAR for Post-Coal Mining Subsidence
Study Area/Context
SAR Data Used
InSAR Technique(s)
Software Used (if specified or implied)
Key Findings (e.g., Max Subsidence Rate, Observed Phenomena)
Musan mine, North Korea (open-pit)
Sentinel-1 C-band (2016-2022)
ICOPS (InSAR time series)
Not specified (machine learning post-processing mentioned)
Max avg rate >15 cm/year, total 170 cm subsidence. 12
Gol Gohar Sirjan Mine (open-pit)
ENVISAT
PS-InSAR
StaMPS
Subsidence -3.6 to 3.6 mm/year. 29
Open-pit slope stability monitoring
Sentinel-1A/B
SBAS
MintPy
Objective to observe slope creep and accelerating creep. 84
Ordos, Inner Mongolia, China (coal mining)
ALOS-PALSAR, ALOS-2 PALSAR-2 (2006-2015)
D-InSAR
Not specified (ISCE mentioned in context)
Max LOS subsidence -65 mm/year, cumulative -246 mm. 13
Lahore City, Pakistan (groundwater/mining)
Sentinel-1 (2018-2019)
PS-InSAR
SNAP/StaMPS
Displacement -114 to 15 mm/year, max subsidence in central part. 30
Çöpler Gold Mine, Türkiye (landslide)
Not specified
SBAS-InSAR
GMTSAR
Continuous slow deformation up to 60 mm/yr, cyanide leach pond 85 mm/yr. 2
Anhui province, Eastern China (abandoned coal mining)
Not specified
SBAS (implied)
PyRate (implied by query context)
373 ha land destroyed, max subsidence 1.7m-3.4m. 85
Datong coal mining area, China
Not specified
Multi-temporal InSAR (logistic model)
Not specified (GMTSAR mentioned in context)
Max subsidence 1.26m (2007-2009). 14
Poland (underground mining)
Not specified
SBAS
Not specified (GMTSAR implied by query context)
Subsidence velocity analyzed with Knothe-Budryk theory. 77
Lakhra coal mines, Pakistan
Sentinel-1A (2018-2023)
SBAS-InSAR, stacking-InSAR
Not specified (SNAP implied by query context)
Max cumulative subsidence -114 mm (SBAS), -19 mm/year (stacking). 11
Midroc Gold Mine Company
C-band SAR
PS-InSAR
StaMPS
Max ground deformation -90 mm/year, cumulative -200 mm (ascending), -100 mm (descending). 15
Banji Coal Mine
D-InSAR, SBAS, UAV tilt photogrammetry (2021-2022)
D-InSAR, SBAS
Not specified (MintPy implied by query context)
Integrated approach enhances monitoring accuracy. 82
Hunchun coal mining area, China
Sentinel-1
DS-InSAR, MSBAS-InSAR
Not specified (ISCE implied by query context)
Significant mining-induced subsidence, uplift related to groundwater. 7

These case studies collectively demonstrate the proven applicability of InSAR for monitoring diverse types of land subsidence in various mining environments. They highlight the wide range of subsidence magnitudes (from millimeters per year to meters) that InSAR can detect and the various SAR missions (e.g., Sentinel-1, ALOS-PALSAR, ENVISAT) that have been successfully utilized. This empirical evidence validates the proposed methodologies for PhD research and provides concrete examples of expected outcomes and the challenges that can be addressed using these open-source tools.

5. Conclusion and Recommendations for PhD Research

5.1. Summary of Key Software Strengths

The landscape of open-source SAR processing software offers a robust suite of tools for monitoring land subsidence in post-coal mining areas using SBAS and PS-InSAR techniques. Each software package possesses distinct strengths that can be leveraged in a comprehensive research workflow:
ISCE (InSAR Scientific Computing Environment): This tool serves as a powerful, high-performance foundation for SAR data processing and interferogram generation. It is renowned for its accuracy, speed, and broad support for various SAR missions, making it ideal for handling large datasets efficiently.
StaMPS (Stanford Method for Persistent Scatterers): As an advanced tool for PS-InSAR and SBAS time-series analysis, StaMPS particularly excels in identifying persistent scatterers across diverse landscapes and performing robust 3D phase unwrapping, crucial for complex deformation patterns.
MintPy (Miami INsar Time-series software in PYthon): Offering a flexible, Python-based solution, MintPy provides comprehensive time-series analysis capabilities for both SBAS and PS-InSAR. Its high compatibility with various pre-processors and its recognition for NISAR mission validation underscore its analytical power and adaptability.
GMTSAR (Generic Mapping Tools SAR): This robust C-based system delivers an end-to-end InSAR processing solution. It features strong SBAS capabilities and offers powerful visualization options through its deep integration with Generic Mapping Tools (GMT).
PyRate: A user-friendly Python tool specifically designed for SBAS time-series analysis, PyRate provides a clear, sequential workflow and offers direct compatibility with outputs from SNAP, simplifying the initial data preparation steps.

5.2. Tailored Recommendations for PhD Thesis Implementation

For a PhD thesis focused on land subsidence monitoring in post-coal mining areas using SAR data, a strategic approach to software selection and workflow integration is crucial. The following recommendations are tailored to maximize the effectiveness and robustness of the research:
Initial Processing (Interferogram Generation):
For the critical initial steps of generating interferograms and preparing SAR data stacks, ISCE is highly recommended. Its high performance, modularity, and accuracy make it particularly suitable for handling the large volumes of data typically involved in multi-temporal InSAR studies. Alternatively, SNAP can be considered for its user-friendly graphical interface and wide mission support, especially if a more visual and interactive approach is preferred for initial data preparation.
Time-Series Analysis (SBAS/PS-InSAR):
The choice of time-series analysis software should align with the researcher’s programming proficiency and the specific characteristics of the study area:
For Python-centric users: MintPy stands out as an excellent choice. Its comprehensive time-series analysis capabilities (supporting both SBAS and PS-InSAR), flexibility in scripting, and strong community support make it highly adaptable. Its compatibility with various pre-processors, including outputs from ISCE and SNAP, further enhances its utility. As an alternative specifically for SBAS processing, PyRate offers a streamlined workflow within a Python environment.
For MATLAB-tolerant users: StaMPS remains a leading option for advanced PS-InSAR and SBAS analysis. It is particularly effective if the study area includes a mix of stable structures and more distributed scatterers in rural or disturbed settings. However, the commercial license requirement for MATLAB must be carefully considered, as it deviates from the “free software” constraint.
For GMT users: GMTSAR provides a robust, integrated workflow for SBAS, covering the entire process from raw data to visualization. This is particularly beneficial for researchers already proficient with GMT for their mapping and plotting needs.
Addressing Mining-Specific Challenges:
The complex and heterogeneous nature of post-coal mining subsidence necessitates specific considerations to ensure accurate and reliable results:
Data Selection: Prioritize SAR data from missions like Sentinel-1 (C-band) due to its free availability, high temporal resolution, and suitability for time-series analysis. Additionally, if available, consider incorporating ALOS-2 (L-band) data. Its longer wavelength offers better penetration through vegetation and is less susceptible to decorrelation in highly disturbed post-mining areas, which can be crucial for maintaining coherence.
Robust Corrections: Implement rigorous atmospheric corrections to mitigate phase delays caused by atmospheric variations. Tools like TRAIN (for StaMPS), PyAPS (for MintPy), or GACOS data (for GMTSAR) can be employed for this purpose. Simultaneously, ensure the use of high-resolution Digital Elevation Models (DEMs) to accurately remove topographic phase and minimize errors introduced by imprecise terrain data. These corrections are vital for isolating the true deformation signal from noise.
Hybrid Approaches: Given the diverse scattering characteristics and complex deformation patterns often found in post-coal mining environments, a hybrid approach combining the strengths of different tools in a sequential workflow is often advantageous. For instance, using ISCE or SNAP for initial pre-processing, followed by MintPy or StaMPS for advanced time-series analysis, can optimize the handling of various data types and processing stages.
Validation: To ensure the scientific defensibility of the results, it is imperative to integrate InSAR-derived deformation measurements with independent geodetic measurements. Data from GNSS (Global Navigation Satellite Systems), precise leveling, or UAV (Unmanned Aerial Vehicle) photogrammetry can provide crucial validation points and offer a more comprehensive understanding of the deformation, especially in areas experiencing large or complex movements.
Learning and Support:
Actively engage with the vibrant open-source InSAR community through dedicated forums and GitHub repositories. These platforms are invaluable for troubleshooting issues, learning advanced techniques, and staying updated on the latest developments. Leveraging available tutorials, particularly those in Jupyter Notebook format, and collaborating with other researchers can significantly enhance the learning process and project success.
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