Overview
This page showcases computational tools, workflows, and research projects I’ve developed for InSAR analysis, geospatial data processing, and Earth observation applications.
InSAR Processing Workflows
HPC-Scale InSAR Time-Series Processing Pipeline
Technologies: Python, ISCE, MintPy, SLURM, Dask
Developed a scalable workflow for processing multi-terabyte SAR datasets on high-performance computing clusters:
- Automated ISCE processing for Sentinel-1 and ALOS-2 data stacks
- Parallel processing using SLURM job arrays for efficient compute resource utilization
- Integration with MintPy/MiaplPy for advanced time-series analysis
- Covariance-aware phase linking for signal enhancement in challenging terrain
- Automated quality control and validation pipelines
Applications: Landslide monitoring in the Andes, subsidence studies, tectonic deformation analysis
Tropospheric Correction Framework
Technologies: Python, ERA5, MERRA-2, PyAPS, NumPy
Implemented multiple atmospheric correction strategies for InSAR:
- Statistical approaches using empirical phase-elevation relationships
- Physics-based ray-tracing with global weather models (ERA5, MERRA-2)
- Comparative analysis of correction methods for different climate zones
- Automated integration into time-series workflows
Impact: Significantly improved deformation signal quality in high-relief mountainous terrain
Multi-Sensor Integration Projects
Multi-Temporal LiDAR Differencing
Technologies: LAStools, Python, GDAL, CloudCompare
Implemented workflows for terrain change detection:
- LiDAR point cloud processing and DEM generation
- Multi-temporal DEM differencing for displacement quantification
- Integration with InSAR results for cross-validation
- Automated change detection and volume calculations
GNSS + InSAR Integration
Technologies: GAMIT/GLOBK, Python, NumPy
Developed workflows for combining geodetic datasets:
- GNSS time-series processing and quality control
- Co-registration of GNSS and InSAR measurements
- Joint inversion for earthquake deformation modeling
- Uncertainty propagation and error analysis
Research Software Contributions
InSAR Processing Optimization
Contributed to improved processing efficiency for community tools:
- Optimized StaMPS and TRAIN workflows for parallel computing
- Developed Python wrappers for automated batch processing
- Created documentation and tutorials for reproducible analysis
Optical Offset Tracking
Technologies: COSI-Corr, Python, GDAL
Implemented fast displacement mapping for rapid deformation events:
- Automated offset tracking for landslide and glacier studies
- Integration with optical satellite data (Sentinel-2, Landsat)
- Multi-scale analysis for different deformation rates
Specialized Applications
Groundwater Depletion Monitoring
Project: Minab Coastal Plain, Iran
Technologies: InSAR, GRACE data, aquifer modeling, Python
Integrated satellite observations with hydrological modeling:
- InSAR-derived subsidence time series
- GRACE-based groundwater storage change analysis
- Aquifer parameter estimation through inverse modeling
- Spatiotemporal correlation analysis
Status: Published at ICCC Workshop 2025
Earthquake Sequence Analysis
Project: 2022 Zagros Mountains, Iran
Technologies: InSAR, Pyrocko, Kite, Grond, ObsPy
Contributed to multi-sensor geodetic analysis:
- Co-seismic deformation mapping with Sentinel-1
- Fault parameter inversion using InSAR observations
- Integration with seismological data
- Published in Seismica (2023)
Landslide Kinematics Characterization
Project: Central Andes, Argentina
Technologies: InSAR time-series, spatial statistics, Python
Comprehensive study of slow-moving landslides:
- Multi-temporal deformation analysis (2014-2024)
- Seasonal pattern recognition and hydrological correlation
- 2D/3D decomposition of displacement fields
- Landslide classification based on kinematics
Status: Ph.D. dissertation research (ongoing)
Technical Stack
Development Practices
- Reproducible research workflows
- Test-driven development (pytest)
- CLI tool development
- Documentation and code commenting
- Continuous integration for data pipelines
Future Projects
I’m currently exploring:
- Deep Learning for InSAR: Applying CNNs for automated deformation detection and phase unwrapping
- Cloud-Based Processing: Migrating workflows to Google Earth Engine and AWS for scalability
- Real-Time Monitoring: Developing near-real-time landslide warning systems
- Multi-Temporal Fusion: Combining SAR, optical, and LiDAR for comprehensive change detection
Collaboration Opportunities
I’m interested in collaborating on:
- InSAR applications for natural hazard monitoring
- Machine learning for Earth observation
- Geospatial data science projects
- Open-source tool development
📧 Get in touch: mohseniaref@uni-potsdam.de