pygmtsar 2024.1.21

PyGMTSAR (Python GMTSAR) - Easy and Fast Satellite Interferometry For Everyone

Announcements

The e-book, titled 'PyGMTSAR: Sentinel-1 Python InSAR: An Introduction' is now available for the stable PyGMTSAR release on various platforms, including Amazon, Apple, Kobo, and many other bookstores. If you'd like a preview of the content, you can check out the PyGMTSAR Introduction Preview uploaded in the repository.

Currently in development, PyGMTSAR2 (Python InSAR) is aimed at experts in Sentinel-1 Satellite Interferometry, offering features such as SBAS, PSI, PSI-SBAS, and more. It is available in pygmtsar2 branch. I share Jupyter notebook examples on Patreon and updates on its progress through my LinkedIn.

You have the option to support the development of PyGMTSAR software on Patreon and Buy Me a Coffee platforms. These platforms also offer additional documentation and use cases.

PyGMTSAR (Python InSAR) - Sentinel-1 Satellite Interferometry for Everyone

This repository is based on a fork of the original GMTSAR and has been extended with my patches for binary tools and the Python library PyGMTSAR, located in the pygmtsar repository branch. I regularly commit my changes to the binary tools back to the GMTSAR upstream repository. This allows users to install the original GMTSAR master branch alongside the PyGMTSAR Python package using PIP. Once PyGMTSAR becomes independent of GMTSAR tools, it will be split into a separate repository.

The goal of the project is to provide easy and fast satellite interferometry (InSAR) processing for Sentinel-1 radar scenes across various environments such as local hosts, cloud environments like Google Cloud VM and AI Notebooks, Amazon EC2, free-of-charge cloud environments like Google Colab, and Docker images. GMTSAR binary command-line tools are used under the hood, but all GMTSAR scripts and GMT commands are replaced by Python code using modern and robust algorithms.

PyGMTSAR Docker Images on DockerHub

See Docker basic image for merged subswaths and cropped scenes and SBAS time series processing on DockerHub PyGMTSAR for Everyone This image is the right choice to start and perform lots of common interferometry tasks.

PyGMTSAR Large Docker Images on DockerHub

See Docker images for multiple stitched scenes and long SBAS time series processing on DockerHub PyGMTSAR for Experts

PyGMTSAR Live Examples on Google Colab

These notebooks provide interactive examples directly in your web browser. All steps are automated, including software installation on Google Colab's cloud host, downloading of Sentinel-1 orbit files, SRTM DEM (and its conversion to ellipsoidal heights using the EGM96 model), a landmask (to mask low-coherence water surfaces), Sentinel-1 SLC scenes from the Alaska Satellite Facility (ASF) datastore, and of course, the complete interferometry processing and result mapping.

Notebooks to Compare Results with GMTSAR, SNAP, and GAMMA Software

ASF Downloading 2017 Iran–Iraq Earthquake vs GMTSAR GAMMA SNAP Co-Seismic Interferogram This notebook downloads Sentinel-1 scenes from the Alaska Satellite Facility (ASF) and compares the results to GMTSAR, SNAP, and GAMMA software. Note: Replace the scene names to generate an interferogram and LOS displacement for your area of interest.

Live Example: S1A_2016_Kumamoto Earthquake_Co-Seismic Interferogram vs ESA Sentinel 1 Toolbox on Alaska Satellite Facility. This example demonstrates single subswath processing with a landmask applied to the interferogram, unwrapped phase, and LOS, east-west, and vertical displacement results.

Live Example: S1AB 2021 Crete Earthquake Co-Seismic Interferogram vs Centre of EO Research & Satellite Remote Sensing, Greece Report This example features the processing of a single cropped subswath with a landmask applied to the interferogram, unwrapped phase, and LOS, east-west, and vertical displacement results.

GMTSAR example dataset S1A_Stack_CPGF_T173 This example demonstrates the SBAS analysis and detrending approach used to remove atmospheric noise, resulting in significantly improved outcomes.

GMTSAR example dataset S1A_Stack_CPGF_T173 This example demonstrates the SBAS analysis and detrending approach to remove atmospheric noise for improved results. Additionally, an OpenStreetMap roads mask is used to unwrap and analyze only the roads with a buffer around them.

More Complex Notebooks Still Available on Google Colab

The notebooks processing more than a single subswath or scene. It's possible on Google Colab limited resources using prepared datasets produced by PyGMTSAR "backup" command described in the notebooks.

ASF Downloading 2020 Ardabil, Iran Earthquake Co-Seismic Interferogram and LOS Displacement The notebook downloads Sentinel-1 Scenes from Alaska Satellite Facility (ASF) to crop the area and merge subswaths and detrend results. Note: replace the scene names to produce an interferogram for your area of interest.

ASF Downloading 2023-02-06 Türkiye Earthquakes Co-Seismic Interferogram and LOS Displacement Projections The notebook downloads Sentinel-1 Scenes from Alaska Satellite Facility (ASF) to stitch 3 scenes and merge subswaths and detrend results. Here are some tricks used to process the large amount of data on Google Colab. Note: replace the scene names to produce an interferogram for your area of interest.

Long Timeseries Analysis is not available on Google Colab

Check out the separate GitHub repository for the Yamchi Dam area dynamic model, YamchiDam. The software tools PyGMTSAR and N-Cube ParaView plugin for 3D/4D GIS Data Visualization are combined for comprehensive 4D analysis and visualization. Explore these repositories to learn more about the tools and methods used in the Yamchi Dam area dynamic model.

Learn more

• Documentation: https://mobigroup.github.io/gmtsar/

• Issue tracker: https://github.com/mobigroup/gmtsar/issues

• Source code: https://github.com/mobigroup/gmtsar

• Docker Images: https://hub.docker.com/repository/docker/mobigroup/pygmtsar

• PyPI Python library: https://pypi.org/project/pygmtsar/

About me

I have STEM master’s degree in radio physics and in 2004 I have got the first prize of the All-Russian Physics competition for significant results in forward and inverse modeling for non-linear optics and holography, also applicable for modeling of Gravity, Magnetic, and Thermal fields and satellite interferometry processing. And I’m data scientist and software developer with 20 year’s experience in science and industrial development. I had been working on government contracts and universities projects and on projects for LG Corp, Google Inc, etc.

Geological models on YouTube channel

Augmented Reality (AR) Geological Models

GitHub repositories

DockerHub repositories

English posts and publications on LinkedIn

English publications on Medium

Russian publications on Habr

@ Alexey Pechnikov, 2023