Overview of the EO1 Mission

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Spectral Science Applications
Terrestrial Carbon Mission
SpectraSat CM
Investigating the use of spectral information in
the reflective region (0.4 2.5 µm) to assess
significant contributors to the global carbon
budget.
Science Theme Carbon Sequestration Ecosystem
response to disturbance and
recovery Vegetation functional
groups Applications Theme Carbon
Management Wildfire Detection Wildfire
Remediation
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SpectraSat GCM Carbon Cycle Science

• Investigate important Carbon Science Hypotheses
  related to
• Ecosystem response to disturbance and recovery
• Decompose the scene into continuous fields of
  sunlit shaded over story, sunlit shaded under
  story, hence biomass density.
• Vegetation functional groups
• Decompose the scene into continuous fields of
  percent bare, herbaceous, water, trees
  (deciduous, evergreen, broadleaf, needleleaf)

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SpectraSat GCM Objectives

• Global measurements of
• Fractional vegetation cover
• Abundance of functionally distinct plant types
• Vegetation condition and ecosystem responses to
  disturbance
• The missions scientific goal is to reduce
  uncertainty in carbon storage, fluxes, and state.
  The mission will map the functional differences
  that determine how global ecosystems respond to
  change. These data are needed to predict carbon
  sources and sinks, to understand coupling of the
  carbon cycle and hydrologic cycle, and to improve
  predictions of climate variability and change.

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Why are cover and functional types important?

• Carbon stocks dry mass of plant structure is
  50 carbon.
• Fractional cover correlates with biomass in open
  (lt 50 cover) vegetation. Most vegetation is
  open.
• Functional types differ in carbon residence times
  and turnover.
• Functional types differ in water and nutrient use
  efficiencies, nutrient cycling rates, and
  sensitivity to climatic stress.

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Measured
Fractional Vegetation and Bare Substrate Cover
Observed
Disturbance Responses
Plant Functional Groups
Biomass Structure Fuels
Canopy Water Chemistry
Carbon Storage Fluxes
Modeled
Interpreted
Core mission data products Other mission data
products Science discipline team
products Integrated understanding
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Fractional Vegetation and Bare Substrate Cover
Measured
Observed
Disturbance Responses
Plant Functional Groups
Biomass Structure Fuels
Canopy Water Chemistry
Carbon Storage Fluxes
Modelled
Interpreted
Core mission data products Other mission data
products Science discipline team
products Integrated understanding
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Carbon Cycle and Ecosystems
Integrated global analyses
Human-Ecosystems-Climate Interactions (Coupling,
Model-Data Fusion, Assimilation)
Sub-regional sources/sinks

Funded
T
High-Resolution Atmospheric CO2
Unfunded
Carbon export to deep ocean
Profiles of Ocean Particles
T
Partnership
Models w/improved ecosystem functions
T Technology development
Physiology Functional Groups
T
Process controls identified errors in sink
reduced
Southern Ocean Carbon Program
Field Campaign
T
Reduced uncertainties in fluxes and coastal C
dynamics
New Ocean Carbon / Coastal Event Observations
Goals Global productivity and land cover change
at fine resolution biomass and carbon fluxes
quantified useful ecological forecasts and
improved climate change projections
Vegetation 3-D Structure, Biomass, Disturbance
T
Terrestrial carbon stocks species habitat
characterized
CH4 sources characterized and quantified
Global CH4 Wetlands, Flooding Permafrost
Knowledge Base
Global Atmospheric CO2 (OCO)
Regional carbon sources/sinks quantified for
planet
N. American Carbon Program
N. Americas carbon budget quantified
Effects of tropical deforestation quantified
uncertainties in tropical carbon source reduced
Land Use Change in Amazonia
2002 Global productivity and land cover
resolution coarse Large uncertainties in
biomass, fluxes, disturbance, and coastal events
Models Computing Capacity
Process Understanding
Case Studies
Improvements
P
Land Cover (Landsat)
Land Cover (LDCM)
Land Cover (LDCM II)
Systematic Observations
Ocean Color (SeaWiFS, MODIS)
Ocean Color/Vegetation (VIIRS/NPP)
Ocean/Land (VIIRS/NPOESS)
Vegetation (AVHRR, MODIS)
Vegetation, Fire (AVHRR, MODIS)
IPCC
IPCC
2010
2012
2014
2015
2008
2002
2004
2006
920
Global C Cycle
Global C Cycle
NA Carbon
NA Carbon
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VNIR-SWIR Spectral Deconvolution
AVIRIS-30m MC Unmixing
Hyperion-30m MC Unmixing
Central Argentina Arid/semi-arid test site
Green Vegetation
Dead/Senescent Vegetation
Bare Soil
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High-Precision Surface Cover AnalysisUsing VNIR
red-edge andSWIR (2000-2400 nm) Spectra Only
SWIR Field Data Shown Herex
Tied Field Spectra Showing Full Arid/Semi-arid
Site Variability
Tied at 2080nm
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VNIR-SWIR Spectral Deconvolution Accuracy
Assessment Using the Same Physical Model But with
AVIRIS, Hyperion and ETM Data
AVIRIS at 30m
Hyperion
Landsat
AVIRIS is highly accurate
Landsat is inaccurate
Hyperion is accurate
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Photosynthetic vs Non-photosynthetic Vegetation
Soil - Fractional Cover
PV
NPV
0.0 0.7
0.3 0.95
Soil
0.0 0.3
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SpectraSatWhat we have going!

• ESE Science driven mission concept with
  significant well defined primary (Carbon Cycle)
  goals uniquely achievable through Spectral
  Imaging.
• Well defined candidate instrument design (JPL
  GIS)
• Well defined candidate spacecraft bus (SMEX)
• Strong operations/technology heritage (EO-1)
• Fits well within the ESSP funding envelope
• Eager cross-cutting Science Team candidates
• Highly productive end-game strategies
• Commercial partner phase-in scenario /OR
• International/Inter-agencies partnerships
  in-kind

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SpectraSat GCM(Spectral Science Applications
Terrestrial Global Carbon Mission)
Baseline Mission Attributes

• Landsat-like sun-synchronous orbit (on WRS) and
  IFOV
• Up to 5 acquisitions per site every 16 days (3
  near-nadir)
• Solar and Lunar Calibration / Atmospheric limb
  scan
• Capacity for 120 collects per week
• Collect is 40 to 60 km wide by multiple of 40 km
  long (up to 1200 km in length)
• Up to 4 stabilized acquisition opportunities per
  path
• Semi-autonomous operations (cost reduction)
• Optional 10 m pan band(s) for enhancing
  observations and providing image aided yaw
  steering
• 320 - 640 GB onboard intelligent storage
  coupled to 450 - 750 MB/sec downlink capability

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Baseline Imaging Spectrometer Characteristics
SpectraSat CM
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Sources of Spectral-Spatial-Non-uniformity
Cross Track Sample
Grids are the detectors Spots are the IFOV
centers Colors are the wavelengths
Wavelength
Requirement
Failure by Frown
Failure by Twist
Failure by Spectral-IFOV-Shift
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Earth Imaging Spectrometer
Unobscured F/2.7 TMA Telescope
Slit
JPL e-beam curved grating
HgCdTe Detector 640 by 211
With three spectrometers Spectral 400 to 2500
nm _at_ 10 nm Spatial 60 km swath _at_ 30 m
JPL uniform F/2.7 Offner Spectrometer
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SpectraSat GCM(Spectral Science Applications
Terrestrial Global Carbon Mission)
Instrument Lunar Orbiting Capability

• Can provide Spectral Imaging at 6 m GSD with
  current instrument IFOV and sampling frequency
  from an altitude of 150 km above the lunar
  surface (reduced SNR).
• Can provide 10 m GSD at an altitude of 235 km
  with current IFOV by reducing sampling frequency
  from 276 to 179 samples/sec (increased SNR).
• Although no alteration zones present on lunar
  surface, should be able to detect presence of
  some important minerals such as titanium.

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Hyperion Maps Mt. Fitton Geology
Hyperion-based apparent reflectance compares with
library reference spectra
Hyperion Spectra
Reference Spectra
(a)
(b)
Hyperion surface composition map agrees with
known geology of Mt. Fitton in South Australia
(1) Published Geologic Survey Map (2) Hyperion
three color image (RGB) showing regions of
interest (3) Hyperion surface composition map
using SWIR spectra above
Courtesy of CSIRO, Australia
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Hyperion Maps Mt. Fitton Geology
Automatic mineral mapping algorithm creates, in
30 seconds, a quick-look mineral map (left
centre). More precise detail is on right.
(Courtesy of CSIRO Australia)
Mineral Spectra
Detailed Talc- Tremolite Map
Mineral Map
Colours of spectra match the thematic image to
left.
Colours to the right indicate the relative
abundance of talc/tremolite . Red shows areas
of greatest abundance and blue gives the
least.
2.1
2.3
2.2
Wavelength(microns)
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EO-1 has viewed the moon!
(EO-1 ALI Pan band)
(EO-1 Hyperion)
Typical Spectrum
Sample Spectra
Full Moon
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Baseline Imaging Spectrometer Characteristics
SpectraSat LM
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Estimated Resources - power/mass/volume/cost
SpectraSat LM

• Power 50 watts
• Mass 40 kg
• Volume lt 0.4M X 0.75M X 0.66M
• Cost 30 M
• Size estimates based on Hyperion
• Cost estimate based on SpectraSat CM

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Investigate VNIR/SWIR Spectral Separability
ofCommon Lunar Rock-Forming Minerals

• Mare
• Pyroxenes (Fe,Mg, Ca silicates)
• Olivine (Fe, Mg silicate)
• Plagioclase Feldspar (Na Ca Al silicates)
• Ilmenite (Fe Ti oxide)
• Cristobalite, Tridymite (Silica)
• Metallic Iron
• Spinels (Mg, Fe, Cr oxides)
• Troilite (Iron Sulphide)

• Highlands
• Plagioclase Feldspar (Na Ca Al silicates)
• Pyroxenes (Fe,Mg, Ca silicates)
• Olivine (Fe, Mg silicate)
• Alkali feldspar (K Al silicate)

Water-rich minerals are lacking Micas,
Amphiboles, Clays, Hematite, Serpentines,
Sulfates, Carbonates, Ice
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Common Lunar Rock-Forming Minerals are Spectrally
Separable in the VNIR/SWIR!
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Industrial International participation

• Industry Partnership Plan being developed
• See white paper draft.
• Considering an end of mission concept that would
  turn over operations and some data rights to a
  commercial entity.
• Discussions are also on-going to gauge US
  industry interest in the mission.
• There is much enthusiasm on the part of Canadian
  and European agencies for hyperspectral remote
  sensing.
• Science complements European SPECTRA mission.

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Project Organization

• PI
• Stephen Ungar, 923Mission Science and Technology

Co-PI Gregory Asner, Stanford Univ. Science
Team Leader
Robert Green, JPL Instrument Scientist and
Calibration
Robert Knox, 923 Deputy Mission Scientist
Science Team University, Government, Industry
Members 10
Project Manager (Code 400) Systems Engineer N.
Speciale (Code 500) Operations Manager Dan Mandl
(Code 500)
External Science Working Group (unfunded) 8
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Science Team Members

• Stephen Ungar, GSFC PI, Project Scientist
• Greg Asner, Stanford Co-PI, Science Team
  Leader. fractional cover, condition
• Robert O. Green, JPL Inst. Science, calibration
• Forrest Hall, UMBC global data products,
  disturbance
• Alfredo Huete, U of Arizona cover and
  condition
• Robert Knox, GSFC Deputy Proj. Sci.
  ecosystem dynamics
• Mary Martin, UNH function and condition (
  biochemistry)
• Betsy Middleton, GSFC function and condition (
  photobiology)
• Dar Roberts, UCSB functional type and condition
  
• Susan Ustin, UC-Davis functional type and
  condition, wetlands
• Others TBD - atmospheric characterization,
  coastal zone