The DESIS Hyperspectral Instrument A New Space-Based Tool for Coastal Zone Monitoring

February 6, 2017 Ray Perkins1, Rupert Müller2, Emiliano Carmona2

1 Teledyne Brown Engineering, 2 Deutsches Zentrum für Luft- und Raumfahrt (DLR)

Teledyne and DLR Partnership

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► Teledyne & DLR, the German Space Centre, have partnered for the DLR Earth Sensing Imaging Spectrometer (DESIS) to be hosted on Teledyne’s Multi-User System for Earth Sensing (MUSES) mounted on the International Space Station (ISS)

► MUSES is an Earth-imaging platform designed, built, owned and operated by Teledyne

• A commercialization project for the ISS • Hosts up to 4 robotically installed & removed instruments • Provides precision pointing for hosted earth observing instruments • Provides all EO mission planning, control, and data downlink • MUSES commercial remote sensing license from NOAA

► DESIS is a Visible to Near-InfraRed (VNIR) Imaging Spectrometer

• Designed and built by DLR • Operated by Teledyne • DLR uses DESIS for scientific research and humanitarian purposes • Teledyne uses DESIS for commercial purposes

Multi-User System for Earth Sensing (MUSES)

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► MUSES Platform ► Located on ELC 4-2 ► Inertially stabilized ► Precise pointing and Earth surface

target tracking ► Up to 4 robotically installed

instruments ► Total data downlink ~225 GB/day ► Teledyne owns the platform,

determines pointing schedules, and retains data rights in cooperation with partners

► Instruments launched in “soft stowage”

MUSES Location on ELC-4

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Teledyne’s Geospatial Solutions

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► Turn-Key Instrument Missions • Development, integration, and operations. • Negotiable Template: 30-month engagement;18-month

integration,12-month flight on MUSES (6 month for primary) • TRL maturation, prove-out of instrument design, early data

products prior to free-flyer missions. ► Space Qualification of Hardware

• Reusable container for the MUSES platform • Standardized power, data, and mechanical interfaces

► Hyperspectral Data Sales • Archive, tasking, subscription • Non-exclusive, limited exclusivity, exclusivity options

► Data Analytics and Decision Information Products • Commercial, scientific, humanitarian value • Collaborative Research and Development • Multi-sensor fusions (MUSES & non-MUSES sensors)

► Payload Operations as a Service • Plan and operate ISS and free-flying satellite payloads from

the Teledyne Tele-Science Center ► Content as a Service

• Host partner geospatial data (imagery, point clouds, etc.) using Teledyne’s AWS cloud-based archive & point of sale infrastructure

► Hosted Workflows • Host partner workflows on Teledyne’s AWS cloud-based

flexible processing architecture

Platform Capabilities

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Pointing Knowledge ≤ 30 arc seconds (~ 60 m on ground from 400 km altitude) Field of Regard 5° outboard cross-track

45° inboard cross-track +/- 25° along- track

Star Tracker Sodern SED26 Inertial Measurement Unit Honeywell Miniature Inertial Measurement Unit (MIMU) Precision Time Sourced from the ISS GPS, ± 250 usec to MUSES

instruments Location knowledge Sourced from the ISS GPS, ± 50 meters, RMS Orbit 51.6° Inclination, 400 km altitude ± 5% (nominal) Data Processing Linux Server on-board ISS with redundant 8 TB storage Daily Downlink Capacity 225 GB

DLR Earth Sensing Imaging Spectrometer

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Characteristic DESIS-30 Features

Ground Sampling Distance 30 m @ 400 km altitude

Ground Swath 30 km @ 400 km altitude

Spectral Range 400 nm – 1000 nm

Spectral Bins Measured: 235 @ 2.55 nm Programmable binning on-orbit

Quantization 12 bits + 1 gain bit

Signal to Noise Ratio @ 550 nm 205:1 sampled at 2.55 nm 406:1 binned to 10.2 nm

On-board calibration Dark Field for DSNU LED Array for PRNU

Independent Pointing Pointing Unit ±15° Along Track

Independent Time and Location On-board GPS

Management of Agricultural and Forest

Ecosystems

Hazard Assessment

Inland Water Quality Monitoring

Drought Impact Assessment

Hyperspectral Application Areas

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► Multi-sensor Data Fusion • Enhance spatial resolution

► Land Use/Land Cover • Land cover classification • Vegetation health assessment • Biomass assessment • Drought impact assessment • Hazard assessment

► Forestry • Forest species identification and

assessment • Forest health monitoring • Biomass assessment

► Aquaculture • Coastal zone monitoring • Water quality monitoring • Oil spill monitoring and assessment

Fusion of Multispectral and Hyperspectral Data

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WV-2 (~2 m, MS 8 bands) Fusion Simulated DESIS (30 m, HSI)

Land Use and Land Cover

► Land cover classification using hyperspectral data • Improved classification accuracy1, 2 • Improved differentiation of crop canopy and crop growth stages3

• Is effective in urban land cover classification4

► Vegetation health assessment • Solar-induced chlorophyll fluorescence5

• Biophysical indicators (Wet biomass, LAI, N, Chlorophyll A & B)6, 7

► Biomass assessment • High potential for estimating biomass of grassland habitats8

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1. Thenkabail P.S.; et. al. Hyperspectral Remote Sensing of Vegetation, CRC Press, Boca Raton, FL, 2012, pp 24-31 2. Wilson, J. H.,; et. al. Separating Crop Species in Northeastern Ontario Using Hyperspectral Data, Remote Sens. 2014, 6, 925-945; doi:10.3390/rs6020925 3. Vijayan, D., Shankar,;et. al. Hyperspectral Data for Land use /Land Cover Classification, ISPPR Archives, Vol XL-8, 2014. 4. Tan, Q.; Wang, J. Hyperspectral Versus Multispectral Satellite Data for Urban Land Cover and Land Use Mapping – Beijing, an Evolving City, ASPRS 2007 Annual

Conference, May 2007. 5. Frankenberg, C., et. al. Remote sensing of near-infrared chlorophyll fluorescence from space in scattering atmospheres: implications for its retrieval and interferences

with atmospheric CO2 retrievals, Atmos. Meas. Tech., 5, 2081-2094, doi:10.5194/amt-5-2081-2012, 2012. 6. Zarco-Tejada P. J.,; et. al. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing

imagery," Agronomy Journal 97(3), 641-653 (2005). 7. Cammarano, D.; et. al. Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments, Remote Sens. 2014, 6, 2827-2844;

doi:10.3390/rs6042827. 8. Psomas, A.; et. al. Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. International

Journal of Remote Sensing, Vol. 32, No. 24, Dec 2011, 9007-9031

Forestry – Monitoring and Assessments

► Forest Stand Mapping • Use of spectral unmixing and endmember classification to characterize

stands in mixed forests1, 2

► Pest Infestation/Damage • Use of spectral signatures to recognize pine beetle infestation3

► Forest monitoring • Generation of above-ground carbon, reforestation, afforestation and

deforestation maps4

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1. Darvishsefat, A.; Kellenberger, T.; Itten, K. Application of Hyperspectral Data for Forest Stand Mapping, Symposium on Geospatial Theory, Processing and Applications, Ottawa, 2002

2. Tong, Q.; Jiao, Q., Zhang, X.: Forest Species Classification Based on Hyperspectral and Multitemporal CHRIS Images, 4th Chris Proba Workshop, Session 3.

3. White, J.; et. al., 2007; Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices, International Journal of Remote Sensing. Vol. 28, No. 10, pp. 2111-2121.

4. Goodenough, D.; et. al., Monitoring Forests From Space: Hyperspectral And Kyoto Products, World Forestry Congress, 2003, http://www.fao.org/docrep/ARTICLE/WFC/XII/0888-B1.HTM.

Aquaculture – Coastal Zone Monitoring (HySpex Hyperspectral Data)

Before Denoising After Denoising

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Starnberger See, Germany Noisy & ‘clean’ bands

Zoomed Areas

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1

[mg/l]

Absorption Estimation (WASI Tool) of Coloured Dissolved Organic Matter

(Error in model fit drops 50% after denoising)

Aquaculture – Estuary and Inland Water Monitoring

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Hyperspectral Imager for the Coastal Ocean (HICO): Application of Space-based Hyperspectral Imagery for the Protection of the Nation’s Coastal Resources, Dr. Darryl Keith (keith.darryl@eps.gov), July 2013

Using atmospherically corrected HICO imagery and a comprehensive field validation program, regionally-tuned algorithms were developed to estimate the spatial distribution of chlorophyll a, colored dissolved organic matter, and turbidity for four estuaries along the northwest coast of Florida from April 2010 – May 2012.

“HICO helped show us that it is possible for a hyperspectral space-based sensor to produce products that meet the needs of EPA.”

Chlorophyll a Turbidity Colored Dissolved Organic Matter

Oil Spill Identification and Tracking

► Adding VNIR hyperspectral imagery to radar can:

• Extract oil spill chemical composition signatures1

• Aid in spill classification1 and reduce the false alarms2 of suspected oil spills3

• Reduce errors in low and high winds2, 4

• Detect oil spills below the surface5

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1. Salem, F.; Kafatos, P. Hyperspectral image analysis for oil spill mitigation. Proc. ACRS 2001—22nd Asian Conference on Remote Sensing, 5-9 November 2001, Singapore, vol. 1 (pp. 748– 753)

2. Mityagina, M.; Lavrova, O. Satellite Survey of Inner Seas: Oil Pollution in the Black and Caspian Seas. Remote Sens. 2016, 8, 875; doi:10.3390/rs8100875. 3. Brekke, C.; Solberg, A. Oil Spill Detection by Satellite Remote Sensing. Remote Sensing of Environment 95 (2005), 1-13; DOI: 10.1016/j.rse.2004.11.015. 4. Ferraro, G.; et. al. Long Term Monitoring of oil spills in European Seas. International Journal of Remote Sensing, Vol. 30, No. 3, 10 February 2009, 627–645 5. Otremba, Z. Oil Droplet Clouds Suspended in the Sea: Can They Be Remotely Detected? Remote Sens. 2016, 8, 857; doi:10.3390/rs8100857.

Current MUSES and DESIS Status

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► MUSES • Completed closeout of pre-launch verifications • Completed pre-launch performance characterizations • Ship to launch site 6 February, 2017 • Manifested for launch on SpaceX-11, April, 2017 • MUSES commissioning during Q3/Q4, 2017

► DESIS • Critical Design Review completed June 2016 • Planned launch on SpaceX-13, Q4, 2017 • DESIS commissioning during Q1/Q2,2018

www.teledyne.com 16

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Backup Slides

Earth Observation From the ISS – Why It Works

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► Coverage of ~90% of populated Earth

► Coverage of ~100% of ocean shipping lanes and major navigational ports

► Coverage of 100% of tropics and equatorial region

► Sophisticated spacecraft bus with required resources

► Upgrade and exchange of instruments as technology and/or markets evolve

► Traditional barriers to entry minimized

MUSES Imaging Opportunity Analysis

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• Selected 12 point targets • From ~50° North to ~50° South latitude • Generally at 10° latitude intervals

MUSES Imaging Opportunities

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DLR Earth Sensing Imaging Spectrometer

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► Teledyne and DLR have partnered to build and operate the DLR Earth Sensing Imaging Spectrometer (DESIS) from the Teledyne-owned MUSES Platform on the ISS

► Teledyne retains rights for commercial use

► DLR retains rights for scientific use

► Launch planned for Q4, 2017

► The DESIS Instrument will be used to • Enable scientific RESEARCH • Expand HUMANITARIAN response • Provide COMMERCIAL value

DESIS Signal to Noise (SNR)

22 SNR for 2.55 nm sampling distance and spectral binning by factor 4

DESIS Pointing Unit

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► Changes sight ±15° in the along-track direction

► Earth Sensing Mode • 11 measurement positions ±15° (every 3°) • Repeatability / accuracy 20 arc minutes • Target replacement time ≤ 0.5 seconds

► Stereo Mode • Collection of up 3 image tiles at different

angles • Used for BRDF & altitude extraction

► Forward Motion Compensation Mode (experimental)

• Used to increase SNR for specific targets • Speed 0.6 deg/sec and 1.5 deg/sec • Accuracy 0.06 degrees (1/10 GSD) • Range of rotation ±15°

TBE GS

Partnership: DLR and TBE Data Processing

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Teledyne Tele-Science Center

DLR GS

DLR DESIS Data Management System

Data Storage DESIS Data + Auxiliary Data

Data Request

L1B – ToA Correction

L1C - Orthorectification

L2A – Atm. Correction

DLR Requestor

L1B – ToA Correction

L1C - Orthorectification

L2A – Atm. Correction

L1A – Transcription

Order Management

Catalog DESIS Archive +

Auxiliary Data

Tasking

Image Data & Telemetry

Tasking

Data Request Products

DESIS data + Auxiliary data

Image Data & Telemetry

L1A – Transcription

Products

Share Calibration Share Validation

Teledyne Earth Sensor Portal

Processing

Commercial Customers Scientific Partners

Processing Chain

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• Extract & evaluate dark current data • Tiling & reformatting of raw HSI • Processing of Calibration Data • Screen raw data • Derive dead/suspicious pixel mask • Generate quicklook image • Metadata generation

L1A

• Input L1A HSI tiles + dark current data • Apply systematic and radiometric

corrections (housekeeping and AOCS data appended).

• Extract quality indicators • Top of Atmosphere radiance.

L1B

• L1B data is orthorectified and resampled • Direct georeferencing using DEM • Map projection.

L1C

• Atmospheric corrections using ATCOR • At Surface reflectance L2A

Long Term Archive L1A Data

Level 1A Processor Transcription

Earth Imagery Experimental Imagery

Calibration Measurements

Calibration & Reference Products

Position & Attitude Products

Auxiliary Data

Screening

In-flight Calibration

Process (offline)

Calibration & Reference Products

Update Cal Tables

Level 1B Processor Systematic &

Radiometric Correction L1B Product

Level 1C Processor Orthorectification L1C Product DEM Database

REF Database

Level 2A Processor Atmospheric Correction

L2A Product Atmospheric LUT