We document one of several methodologies used to validate the NASA-ISRO Synthetic Aperture Radar (NISAR) mission requirements for solid earth deformation. NISAR’s deformation requirements cover steady-state, coseismic, and transient deformation processes and were designed to confirm that the mission is able to meet its solid earth science goals. We use independent observations of earth surface deformation from continuous Global Navigation Satellite System (GNSS) stations as ground truth for NISAR-observed deformation, and we provide a statistical framework to assess the quality of the associated NISAR data products. Our validation workflows have been developed as Jupyter Notebooks and are publicly available via GitHub/GitLab.

Surface Topography and Vegetation (STV) is a NASA targeted observable for maturation into an observing system architecture. STV will acquire high-resolution, global height measurements, including bare surface land topography, ice topography, vegetation structure, and shallow water bathymetry. These measurements serve a broad range of science and applications objectives that span solid earth, cryosphere, biosphere and hydrosphere disciplines. A common set of measurements could meet many of the community needs. STV objectives would be best met by new observing strategies that employ flexible multi-source and sensor measurements from a variety of orbital and sub-orbital assets. Science and application objectives would be best met by new, 3-dimensional observations from lidar, radar, and stereoimaging. Simulations, experiments, data analysis and technology development in interferometric SAR, lidar and stereo photogrammetry approaches, platform options and system architectures will all mature STV toward an observing system.

We establish a workflow for validating NISAR Solid Earth Science (SES) products based on UAVSAR measurements of secular velocities and coseismic displacements across earthquake faults. UAVSAR is an L-band synthetic aperture radar capable of measuring solid Earth deformations through repeat pass interferometry. High spatial resolution makes UAVSAR especially sensitive to surface deformation at short spatial wavelengths (e.g., near a crustal fault). Furthermore, UAVSAR acquisition schemes can provide a 3D picture of deformation. For NISAR validation, secular (interseismic) deformation will be measured across the creeping section of the San Andreas fault, where fault creep presents a well-defined tectonic signal and a rich UAVSAR data archive exists. The UAVSAR measurements will be quantitatively compared to measurements based on satellite InSAR data. The workflow developed herein is similar to that in the SES algorithm theoretical basis document (ATBD) for validating NISAR products, with changes made to account for the peculiarities of UAVSAR data.

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility – all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid Image Analysis (ARIA) project’s Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

The Snow Water Equivalent (SWE) is identified as the key element of a snowpack that impacts rivers' streamflow and water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a large time series of Sentinel-1 data from winter 2021. The retrieved SWE change observations align really well with in situ stations with 0.82 correlation and 0.76cm RSME. The total retrieved SWE also align really well with 16 in situ values in the scene with less than 20cm SWE error. On the other hand, the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5.