The Future of Scientific Computing for InSAR Geodetic Imaging

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1 The Future of Scientific Computing for InSAR Geodetic Imaging Paul A. Rosen Jet Propulsion Laboratory California Institute of Technology October 29,

2 Outline Background geodetic imaging measurements Geodetic imaging science, present and future Anticipated missions and data sets Implications for computational infrastructure and capabilities - 2 -

Differential Interferometry for Change Two observations are made from different

proportional to topography and topographic change.

space First Pass, time t 1 t 1 (t 2 ) top o (t 1 ) d LOS Second Pass, time t 2 Df -

3 Differential Interferometry for Change Two observations are made from different locations in space and at different times, so the interfero-metric phase is proportional to topography and topographic change. B t 2 Assumes surface element remains geometrically and electrically similar Radar in space First Pass, time t 1 t 1 (t 2 ) top o (t 1 ) d LOS Second Pass, time t 2 Df - differential phase r - range l - wavelength B - baseline t - time t 1 t 2 Df = 4p l (r(t 1)- r(t 2 )) = 4p l (d LOS - Dr topo )+ noise - 3 -

4 Basic Geodetic & Imaging Products Polarimetry (Africa) Interferogram (Taiwan) Correlation (California) Simons & Rosen (2007) - 4 -

~40 km EarthScope/EarthCube Workshop, Arizona State University,

Understand Earthquake Mechanisms Parkfield CARH GPS Station PBO

5 ~40 km EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 Dense Sampling in Space and Time to Understand Earthquake Mechanisms Parkfield CARH GPS Station PBO Western US permanent stations 1 month Gomberg et al. (2010) doi: /b month Millions of GPSlike points in each image frame. Frequent temporal snapshots will reveal new processes and improve models - 5 -

6 Spatial Synoptic coverage 2010 Mw 8.8 Maule (Chile) Lin et al. (2012) Submitted to JGR - 6 -

geodetic measurements can potentially support more complex geophysical models, provided: -

7 Long Valley Deformation Time Series MInTS analysis, ECMWF-corrected New methods for InSAR time series analysis are becoming mature, and are giving a new view of Earth processes These geodetic measurements can potentially support more complex geophysical models, provided: - Data are available - Data have suitable quality Agram, Jolivet, Simons et al (2012 in prep) - 7 -

8 Etna Deformation Time Series NSBAS analysis, ECMWF-corrected With appropriate data, methods can be applied to understanding eruption dynamics on global scale Agram, Jolivet, Simons et al (2012 in prep) - 8 -

9 Hydrology with InSAR Time Series Credits: InSAR analysis Zhen Liu, Tom Farr (JPL); Visualization Vince Realmuto (JPL) -

Assessing Devastation with Remote Sensing:

Proxy Map indicates significant centimeter

[1] Canterbury TV Re-zoning map indicating

This map is the product of many Building

10 Assessing Devastation with Remote Sensing: Christchurch, NZ In Hours to Days Radar Remote Sensing In 4 Months Engineering Assessment Christchurch Cathedral The Damage Proxy Map indicates significant centimeter scale change due to subsidence, inundation, or structure collapse allowing early assessment of the scale of damage. [1] Canterbury TV Re-zoning map indicating where repair and rebuilding will be allowed. This map is the product of many Building man-hours of effort to assess structures and the land that they were built on. [2]

deformation can lead to better understanding of

11 Landslide occurrences correlated to earthquake localities Comprehensive landslide mapping through deformation can lead to better understanding of landslide mechanisms worldwide Scheingross et al. (2012) Accepted to JGR

12 SeaSAT SIR-A SIR-B EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 International SAR Missions The Golden Age of SAR Challenger SIR-C/ X-SAR Earth SRTM InSAR? Magellan Cassini Planetary Launch Orbit Launch Orbit LRO Chandryan ERS-1 ERS-2 JERS-1 RADARSAT-1 Envisat-1 ALOS-1/2 RADARSAT-2/RCM TerraSAR-X TanDEM-X Cosmo-Skymed

Decadal Survey: US L-band Mission Science Recommended

address important scientific questions of high

masses and how does it relate to the climate?

massive release of energy of earthquakes and

Ice sheets and sea level Will there be catastrophic

and West Antarctic and, if so, how rapidly will this

What will be the time patterns of sea level rise as a

Changes in ecosystem structure and biomass How does

How does land use affect the carbon cycle and

What are the effects of disturbance on productivity,

What are the management opportunities for minimizing

Extreme events, including earthquakes and volcanic

13 Decadal Survey: US L-band Mission Science Recommended by the NRC Decadal Survey for near-term launch to address important scientific questions of high societal impact: What drives the changes in ice masses and how does it relate to the climate? How are Earth s carbon cycle and ecosystems changing, and what are the consequences? How do we manage the changing landscape caused by the massive release of energy of earthquakes and volcanoes? Ice sheets and sea level Will there be catastrophic collapse of the major ice sheets, including Greenland and West Antarctic and, if so, how rapidly will this occur? What will be the time patterns of sea level rise as a result? Changes in ecosystem structure and biomass How does climate change affect the carbon cycle? How does land use affect the carbon cycle and biodiversity? What are the effects of disturbance on productivity, carbon, and other ecosystem functions and services? What are the management opportunities for minimizing disruption in the carbon cycle? Extreme events, including earthquakes and volcanic eruptions Are major fault systems nearing release of stress via strong earthquakes? Can we predict the future eruptions of volcanoes? Planned by NASA as one of the following 4 Decadal Survey TIER 1 Missions SMAP ICESat-II DESDynI CLARREO Ice Dynamics Mission Concept Review See export compliance restrictions on cover Biomass Deformation 3-13

14 Coverage Area and Frequency Solid Earth Solid Earth 41,637,823 km 2 ; Every Cycle, A/D; Single-Pol 87,061,332 km 2 ; 2 per yr, A/D; Single-Pol Ice Ecosystems 87,776,900 km 2 ; 1 per seas., Quad-Pol 34,839,285 km 2 ;5 per cycle; Single-Pol

15 Average access latency to reimage any point on Earth with a single 8 day orbiting radar The key is free, open and low latency access to raw data and the infrastructure to exploit it fully for societal and scientific benefit

16 User Communities for SAR Data Wide-range of needs and capabilities Individual Scientists and Modelers - Small areas, specific problems Science Power Users - Large scale problems (interseismic, global carbon, pan-icesheet) Operational Agencies - Ship tracking, water resource monitoring, frac-ing, oil-spills, levees Disaster/hazard response - Earthquakes, volcanoes, fires, floods, etc. Implies flexible and extensible algorithms and processing

Computational Complexity Flowchart for PrepIgramStack Unwrapped IFGs Coherence List of IFGs + Bperp Common Mask Metadata userfn.py Filenames Inputs Parameters data.

17 Computational Complexity Flowchart for PrepIgramStack Unwrapped IFGs Coherence List of IFGs + Bperp Common Mask Metadata userfn.py Filenames Inputs Parameters data.xml GIAnT overview Mask + Crop Metadata includes DEM, Lat, Lon files in radar coordinates Unwrapped IFGs Coherence Common Mask Metadata Multilook Common reference Weather models PrepIgramStack.py GPS data Output: HDF5 file Output: Lat, Lon, DEM ProcessStack.py Condition Data Orbits Condition Data sbas.xml (or) mints.xml Flowchart for ProcessStack HDF5 file from PrepIgramStack data.xml SBAS chain MInTS chain DatatoWavelet.py Form SLC 1 Form SLC 2 Estimate Tie Points Resample Image #2 & Form Interferogram & Estimate Correlation DEM GPS DEM from PrepIgramStack Atmospheric corrections Inputs Empirical multiscale Weather model approach approach Network inversion of PyAPS - every SAR parameters acquisition Lat+Lon +DEM from PrepIgramStack Automatic download of weather model SBASInvert.py (or) NSBASInvert.py (or) TimefnInvert.py InvertWaveletCoeffs.py (or) InvertWaveletCoeffs_fol ds.py IFG corrections WavelettoData.py Remove Model Return Model Remove Topography Filter & Look Down Unwrap Phase Geocode Post-Process & Model (Re)Estimate Baseline Independent Data Orbital error correction Compare GPS to Deramp each IFG each IFG Network or direct Network inversion of inversion of parameters parameters IFG corrections GPS data from SOPAC or file Visualization Output: HDF5 file

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Cumulative Data Volume (GB) EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 The Data Explosion Gigabytes of International

19 Cumulative Data Volume (GB) EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 The Data Explosion Gigabytes of International Civil SAR Data (cumulative) 25,000,000 20,000,000 15,000,000 10,000,000 5,000, Year Speculative

20 Sample Sizing for 350,000 ALOS scenes Processing Steps Processing hours per scene Processing hours for 350K scenes Number of Nodes 1 Common Processing Steps ,000 9 LOS Surface Displacement Maps , AT Surface Displacement Map , Atmospheric Correction ,167 4 Surface Change Map ,500 2 LOS Surface Deformation Time Series , AT Surface Deformation Time series ,875 3 Surface Change Time Series ,833 1 Total , Product Temp Storage (per product) Archived Volume LOS Surface Deformation Maps 20 Gb 735 Tb AT Surface Deformation Maps 20 Gb 735 Tb Surface Change Maps 20 Gb 735 Tb LOS Deformation Time Series ~ Gb 110 Gb AT Deformation Time Series ~ Gb 110 Gb Surface Change Time Series ~ Gb 110 Gb 350,000 scenes =250 Tb

Wavelength Band EarthScope/EarthCube Workshop, Arizona State University, Tempe,

our purposes P L Commercial, or Unavailable Wrong Frequency and/or Commercial

PalSAR-2 (JAXA) UAVSAR/Airborne (difficult processing) US L-band SAR?

Radarsat-2 (CSA) Sentinel-1 (ESA) Indian RISAT (ISRO) Radarsat-C (CSA) X 2000

21 Wavelength Band EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 Added computational complexity: data isn t always optimal for our purposes P L Commercial, or Unavailable Wrong Frequency and/or Commercial and/or unknown Defunct or Unavailable PalSAR (JAXA) Biomass (ESA) SAOCOM (CONAE) PalSAR-2 (JAXA) UAVSAR/Airborne (difficult processing) US L-band SAR? S Chinese HJ-1-C (China) C Radarsat-1 (CSA) ERS-1/2 (ESA) ENVISAT (ESA) Radarsat-2 (CSA) Sentinel-1 (ESA) Indian RISAT (ISRO) Radarsat-C (CSA) X TerraSAR-X (DLR) Tandem-X (DLR) Cosmo/SKYMED (ASI) TerraSAR-X2 (ASI) Cosmo/SKYMED-2 (ASI)

Software and Methods for Diverse Users Processing this sea of data will require modern methods - Computational architecture that can address complex data analysis/processing and

scalable from single users to large computational systems The next generation tools being built have the following attributes: - Python-based, with consequent rich ready-made tool set -

22 Software and Methods for Diverse Users Processing this sea of data will require modern methods - Computational architecture that can address complex data analysis/processing and interoperability of products, processing and models in a heterogeneous web-based environment - Seamless communication between users, data, and models in the field and elsewhere - Software scalable from single users to large computational systems The next generation tools being built have the following attributes: - Python-based, with consequent rich ready-made tool set - Object-oriented - Interoperable - Provenance - Recipes for incorporating legacy code Individual users Cloud systems DAACs Likely basis of US SAR mission production processor Being tested by other large scale projects

23 The future A lot of data many petabytes New homogeneous data sets suitable for global time series - Historical and new heterogenous datasets from legacy systems, small-sats, and other international sensors. Mature Algorithms for time-varying geodetic imaging and geophysical exploitation thereof Desirable future architecture features: Loosely-coupled architecture to enable rapid assimilation of new technology advancements in different components as they develop Elastic compute resources for processing on demand Distributed data storage to facilitate low-latency data access across geographically dispersed compute nodes. Interoperable metadata models and encoding formats that are infused into tools, data systems, and used across different communities

The future cont d More architectural features Federated data discovery and access enabling scalable handling of big data. InSAR data alone cannot be effectively handled by one data center.

24 The future cont d More architectural features Federated data discovery and access enabling scalable handling of big data. InSAR data alone cannot be effectively handled by one data center. Data product preservation and stewardship enabling product provenance, transparency, and reproducibility. Visualization environments with sufficient network and processing bandwidth to enable innovation and discoveries not otherwise possible. Robustness and ease-of-use of geodetic imaging software given variable quality data

25 Synthetic Aperture Radar Small antenna - big beam Synthesized long antenna Big antenna - small beam

26 Some Examples of Data Access Issues for US Investigators ALOS-2 L-band, 14-day repeat: good for US science objectives Multi-mode, campaign mapping like ALOS-1 (as far as we know) Some commercial pressure Radarsat-2 C-band, 24-day repeat: Limited science potential Fully commercial - $3K per image 1M images = $3B Multi-mode Pre-existing orders limit data-taking flexibility Cosmo-Skymed X-band, 1- to 11-day repeat: often good for US science objectives Multi-mode, dual-use limits data-taking flexibility Undersized downlink capacity and distribution system (to date)

State University, Tempe, AZ, October 2012 Polarization -

Polarization Polarization Filters Vertical polarization

Horizontal polarization does not pass through

27 Color figures from EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 Polarization - A Measure of Surface Orientations and Properties Wave Polarization Polarization Filters Vertical polarization passes through horizontally arranged absorbers. Horizontal polarization does not pass through horizontally arranged absorbers. Mostly horizontal polarization is reflected from a flat surface

28 Phase and Radar Interferometry The phase of the radar signal is the number of cycles of oscillation that the wave executes between the radar and the surface and back again. 2p 4p Cycle number Collection of random path lengths jumbles the phase of the echo 1 l 2 l The total phase is two-way range measured in wave cycles + random component from the surface

Radar Interferometry for Topography The two radar (SAR) antennas act as coherent sources When imaging a surface, the phase fronts from the two sources interfere The

29 Radar Interferometry for Topography The two radar (SAR) antennas act as coherent sources When imaging a surface, the phase fronts from the two sources interfere The surface topography slices the interference pattern The measured phase differences record the topographic information Df = 4p l (r(s 1)- r(s 2 )) = 4p l Dr topo + noise

Radar Design to Meet Critical Requirements Repeat Period requirement for Deformation science drives the Radar Swath 12M-day Repeat Period => 240/M-km Swath Width Sensitivity requirement for Biomass

30 Radar Design to Meet Critical Requirements Repeat Period requirement for Deformation science drives the Radar Swath 12M-day Repeat Period => 240/M-km Swath Width Sensitivity requirement for Biomass (cross-pol) measurement drives Antenna Size and Radar Power Accuracy requirements for Deformation and Biomass drive Electronics & Mechanical Stability and Calibration A new SweepSAR technique was adopted as a means to achieve much wider swath than conventional SAR strip-mapping, without the performance sacrifices associated with the traditional ScanSAR technique Conventional StripMap: <~70km Swath Radar antenna Conventional ScanSAR: non-uniform along-track sampling Radar antenna Resulting ~40 day repeat does NOT meet proposed Deformation and Ice Science Requirements Resulting degradation in effective azimuth looks does NOT meet proposed Ecosystem Science Requirements

New SweepSAR Technique to Meet Science Needs On Transmit, all Feed Array elements are illuminated (maximum Transmit Power), creating the wide elevation beam On Receive, the Feed

swath DBF allows multiple simultaneous echoes in the swath to be resolved by angle of arrival Uses large reflector to provide high aperture gain Full-size azimuth aperture for

31 New SweepSAR Technique to Meet Science Needs On Transmit, all Feed Array elements are illuminated (maximum Transmit Power), creating the wide elevation beam On Receive, the Feed Array element echo signals are processed individually, taking advantage of the full Reflector area (maximum Antenna Gain) Uses digital beamforming to provide wide measurement swath DBF allows multiple simultaneous echoes in the swath to be resolved by angle of arrival Uses large reflector to provide high aperture gain Full-size azimuth aperture for both transmit and receive Full-sized elevation aperture on receive Only need data from feed array elements being illuminated by an echoes These elements can be predicted a priori

Period where no data acquired EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 Jakobshavn Isbrae Highly Variable in Time and Space Jakobshavn Isbrae is one of the few

32 Period where no data acquired EarthScope/EarthCube Workshop, Arizona State University, Tempe, AZ, October 2012 Jakobshavn Isbrae Highly Variable in Time and Space Jakobshavn Isbrae is one of the few glaciers where frequent InSAR observations are available. Position of Ice Front ~5%/yr velocity increase Despite spare spatial and sporadic temporal sampling, existing SAR data reveal large variations in glacier flow. Science requires fine temporal sampling of all rapidly evolving outlet glaciers and ice streams

Global mapping of carbon and biomass dynamics at high spatial resolution and high accuracy Global Carbon Monitoring - Determines carbon stored in vegetation and the net effect of changes from fires

in growing forests and losses to existing stocks - Essential to reliable Carbon Monitoring System A US mission would then enable global carbon modeling at spatial scales commensurate with

33 Global mapping of carbon and biomass dynamics at high spatial resolution and high accuracy Global Carbon Monitoring - Determines carbon stored in vegetation and the net effect of changes from fires and other disturbances and subsequent regrowth on concentrations of atmospheric CO 2 - Supports climate treaty implementation by providing spatially explicit estimates of carbon stocks, accumulation in growing forests and losses to existing stocks - Essential to reliable Carbon Monitoring System A US mission would then enable global carbon modeling at spatial scales commensurate with disturbances and environmental gradients - Critical for initializing models that evaluate policy actions by predicting future land/atmosphere carbon dynamics Snapshot of California carbon from available foreign data

34 How Much Data is Enough Data? It depends on perspective: Science - Problem of interest (Time and spatial scales, required accuracy, etc.) - Region of Interest (Vegetation, Access, Cloud cover, etc.) - Resources (available grants, computers, students) Policy/Program National priorities (science or society?) Science priorities (e.g. climate or hazards?, continuity or discovery?) Cost (of buying data vs. building a mission)

SARscape Modules for ENVI

Visual Information Solutions SARscape Modules for ENVI Read, process, analyze, and output products from SAR data. ENVI. Easy to Use Tools. Proven Functionality. Fast Results. DEM, based on TerraSAR-X-1