Day 4

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Paper title First Global Sentinel-1 Interferometric Coherence and Backscatter Metrics in the Cloud empowered by Jupyter-based Visualization and Analytics Tools
Authors
  1. Josef Kellndorfer Earth Big Data LLC Speaker
  2. Oliver Cartus Gamma Remote Sensing AG, Gümligen, Switzerland
  3. Paul Rosen Jet Propulsion Laboratory, California Institue of Technology
  4. Shadi Oveisgharan Jet Propulsion Lab
  5. Marco Lavalle NASA Jet Propulsion Laboratory, California Institute of Technology
  6. Batuhan Osmanoglu NASA Goddard Space Flight Center
  7. Urs Wegmuller Gamma Remote Sensing
  8. Christoph Marnard Gamma Remote Sensing
Form of presentation Poster
Topics
  • Open Earth Forum
    • C5.03 Open Source, data science and toolboxes in EO: Current status & evolution
Abstract text Interferometric SAR observations of surface deformation are a valuable tool for investigating the dynamics of earthquakes, volcanic activity, landslides, glaciers, etc. To evaluate the accuracy of deformation measurements obtained from different existing or potential spaceborne InSAR configurations (different wavelengths, spatial resolutions, look geometries, repeat intervals, etc.), NASA is developing the Science Performance Model (SPM) in the context of the NISAR and follow-on Surface Deformation Continuity missions. The SPM allows for simulating different InSAR configurations and considers the major error sources affecting the accuracy of deformation measurements, such as ionospheric and tropospheric propagation delays or the effects of spatial and temporal decorrelation. In this NASA-funded study, we generated a global temporal coherence and backscatter data set for four seasons with a spatial resolution of 3 arcsec using about 205,000 Sentinel-1 6- and 12-day repeat-pass imagery to complement the SPM with spatially detailed information on the effect of temporal decorrelation at C-band. Global processing of one year of Sentinel-1 Interferometric Wide Swath (IW) repeat-pass observations acquired between December 2019 and November 2020 to calculate all possible 6-, 12-, 18-, 24-, 36-, and 48-day repeat-pass coherence images (6- and 12-day repeat-pass where available) requires fast data access and sufficient compute resources to complete such scale of processing. We implemented a global S1 coherence processor using established solutions for processing Sentinel-1 SLC data. Input data were streamed from the Sentinel-1 SLC archive of the Alaska Satellite Facility and processed with the InSAR processing software developed by GAMMA Remote Sensing (www.gamma-rs.ch) coupled with cloud-scaling processing software employing Amazon Web Services developed by Earth Big Data LLC (earthbigdata.com). The processing was done on a per relative orbit basis and includes co-registration of SLCs to a common reference SLC, calculation of differential interferograms including slope-adaptive range common band filtering, and coherence estimation with adaptive estimation windows, which ensure a low coherence estimation bias of < 0.05. To account for the steep azimuth spectrum ramp in each burst, most of the processing steps are performed in the original burst geometry of the S1 SLCs so that information in the overlap areas of adjacent bursts is processed separately. Terrain-corrected geocoding to the 3x3 arcsec target resolution and simulation of topographic phase relies on S1 precision orbit information and the GLO-90-F Copernicus DEM. Alongside the coherence imagery, backscatter images are processed to radiometrically-terrain-corrected, RTC, level. Seasonal composites of 6-, 12-, etc. coherence imagery as well as RTC backscatter are generated. Based on the coherence values, coherence decay rates were determined per season with an exponential decay model. The processing of the individual coherence images, RTC backscatter images, seasonal coherence and backscatter composites as well as the pixel-level coherence decay modeling results could be completed in about a week with data throughput from SLC to finished tiled products of about 10 TB/hour. The data set is now residing at two open accessible locations, the NASA DAAC at the Alaska Satellite Facility (https://asf.alaska.edu/datasets/derived/global-seasonal-sentinel-1-interferometric-coherence-and-backscatter-dataset/), and the AWS Registry of Open Data (https://registry.opendata.aws/ebd-sentinel-1-global-coherence-backscatter/). A suite of open source visulziation tools have been generated using the python ecosystem to access and visualized this global data set efficiently. These tools take advantage of Jupyter notebook based implementations and efficient metadata structures on top of the openly available data set on AWS. We will present production steps and visuzlization examples in this talk.