Collaborative Research: Elements: Monitoring Earth Surface Deformation with the Next Generation of InSAR Satellites: GMTSAR

Understanding the processes of earthquakes, volcanoes and hydrological changes, and their associated hazards is a top priority of the solid earth research community and USGS, due to the potential for societal disruption, financial consequences, and possible loss of life if not prepared for adequately. This requires not only long-term estimates of such processes and their hazards within a socially relevant timeframe, but also an evaluation on the impact of human activities over the Earth’s surface and interior.

These estimates and evaluations hinge on the capability of accurately measuring how the Earth’s surface changes and deforms over time. For example, it is important to know how fast the seismic moment is accumulating over the San Andreas fault system, as that will tell us where and when will we be expecting the next destructive earthquake. This requires us to be able to measure the deformation that spans hundreds of kilometers at an accuracy of 0.5 mm/yr with resolution better than 10 km.

Interferometric Synthetic Aperture Radar (InSAR) is the best technique for this crucial task, as the current remote sensing satellite observations that inform this technique come with broad-scale coverage, at low-cost, regardless of weather and on a regular basis. However, the upcoming new InSAR missions are raising a new challenge: how to efficiently handle drastically increasing amounts of data (~80 TB per day for the NISAR mission).

To answer this challenge, the freely-available InSAR processing software GMTSAR is developing robust and efficient approaches to take full advantage of the satellite-generated data for both scientific research and societal applications. The main innovations of this project are to enable the cloud computing capabilities, transfer to newer generation of programing language, and keep engaging more users to build their own data processing strategies using this software.

The developers will ensure that users from across the globe have the support they need for access to state-of-the-art processing techniques, and will continue improving the documentation, example datasets and tutorials to strengthen the foundation for education in the field of space geodesy.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for measuring small displacements (1-10 cm) of the surface of the earth including those caused by tectonic loading, earthquakes, volcanoes, landslides, glaciers, ground fluid injection/withdrawal and underground nuclear tests. Over the past decade, a freely available, open-source software has been developed to harness these valuable datasets, which is called GMTSAR.

During past investigations, this software has been equipped with the power to capitalize on the freely available ~1200 TB per year of data from Sentinel-1 satellite operated by the European Space Agency, and was provided as a robust research tool to the user base. The upcoming NISAR mission operated by NASA and ISRO, will dramatically increase the amount of available SAR data to over 30,000 TBytes per year.

While this is a boon for InSAR science, it presents two main processing hurdles: how can one achieve maximum productivity with these increasing large datasets, while still preserving the accuracy of the measurements and how can one best facilitate broad user access to this trove of data. This project addresses these challenges by (1) enabling GMTSAR to permit rapid processing of very large data sets in a cloud computing environment, and (2) further expanding the usage of these InSAR data in both research and student communities by integrating with Python and streamlining processing modules to simplify user interactions.

Facilitating the processing of large InSAR datasets with enhanced GMTSAR software will allow solid earth and cryosphere scientists to utilize the massive InSAR data sets to advance their interdisciplinary investigations, including global observations of volcanoes, estimates of seismic hazard through strain-rate mapping, monitoring urban infrastructure, tracking ice sheet movements, and detecting coastal subsidence. The global reach of InSAR science will be advanced by streamlined processing modules that are accessible to both specialists and students alike.

In addition, the improvements we propose to make to specific modules, including the development of routine integration with Global Navigation Satellite System (GNSS) data and the combination of line-of-sight (LOS) InSAR measurements from both right-looking Sentinel-1 and left-looking NISAR satellites, will improve the accuracy of measurements to enable full 3D vector displacement time series analyses.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.