eo-1/hyperion: nearing twelve years of successful mission science operation and future plans
TRANSCRIPT
EO-1/HYPERION: NEARING TWELVE YEARS OF
SUCCESSFUL MISSION SCIENCE OPERATION AND FUTURE PLANS
Elizabeth M. MiddletonElizabeth M. MiddletonNASA/Goddard Space Flight Center, USANASA/Goddard Space Flight Center, USA
Petya K. E. CampbellPetya K. E. Campbell11, K. Fred Huemmrich, K. Fred Huemmrich11, Qingyuan Zhang, Qingyuan Zhang22,, Yen-Ben ChengYen-Ben Cheng33, David , David LandisLandis44, Stephen Ungar, Stephen Ungar22, Lawrence Ong, Lawrence Ong55, and Nathan Pollack, and Nathan Pollack5 5
1 University of Maryland Baltimore County 1 University of Maryland Baltimore County 2 Universities Space Research Association (USRA)2 Universities Space Research Association (USRA)
3 Earth Resources Technology, Inc.3 Earth Resources Technology, Inc. 4 Sigma Space Corp.4 Sigma Space Corp.
5 Science Systems and Applications, Inc.5 Science Systems and Applications, Inc.
IGARSS’12 IGARSS’12 MO3.3 Spaceborne Imaging Spectroscopy Missions: MO3.3 Spaceborne Imaging Spectroscopy Missions: Updates, and Global Datasets and Products [#4254]Updates, and Global Datasets and Products [#4254]
Munich, Germany , July 23, 2012Munich, Germany , July 23, 2012
Overview of the EO-1 Mission Science Office Activities
Hyperion• Acquisitions and Data Quality Checks• Support New Algorithms (fAPARchl, PRI)• Conduct Field Tests• Comparisons with MODIS results• Conduct Comparisons with Flux Towers
EO-1 Acquisitions, Dec 2000 – Current> 65,275 Hyperion scenes have been collected
Mean reflectance spectra (solid line) Standard deviations (dashed blue line)
Time Series for CEOS Cal/Val Sites
Temporal variation in spectral characteristics, Railroad Valley, NV Similar datasets are being assembled at other CEOS
Cal/Val and LPV sites
Campbell et al. 2012
4 km-1 -0.5 0 0.5 +1
N
N
+ +
Railroad Valley Playa site (cross): A. Natural color composite (RGB: 651,549,447), B. Getis Gi statistics, displaying the homogeneous regions
A. B.
EO-1 Hyperion Image ProcessingLevel 1R Hyperion data were atmospherically corrected using the Atmosphere CORrection Now (ACORN) model.
Reflectance spectra were extracted in the vicinity of the existing flux towers, from 30-50 pixels depending on the site size.
-50
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ACO
RN
ATREM
corn (r = 0.95)forest (r = 0.98)water (r = 0.92)bright target (r = 0.97)lichens (r = 0.98)ice (r = 0.99)
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Refle
ctanc
e (%)
Wavelength (nm)
ice AC ice ATbright target AC bright target ATcorn AC corn ATlichens AC lichens ATforest AC fores ATwater AC water AT
Imagery of cornfield from Airborne Imaging Spectrometer for Applications (AISA) data collected on September 14, 2009. Left panel shows fAPAR from NDVI; middle panel is PRI; and right panel is modeled GEP in mg CO2 m-2 s-1 using the model derived from ground reflectance data.
Scaling Fluxes to Aircraft
DOY 108 172 190 195 231 277
EO-1 HyperionEO-1 HyperionTrue colorTrue color
fAPARfAPARcanopycanopy
2008
SpringSpring SummerSummer FallFall
USDA Cornfield siteUSDA Cornfield site 20082008
fAPARfAPARchlchl
fAPARfAPARNPVNPV
DOY 108 172 190 195 231 277 2008
SpringSpring SummerSummer FallFall
fAPARfAPARleafleaf
fAPARfAPARcanopycanopy
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07/13/08 07/23/08 08/02/08 08/12/08 08/22/08 09/01/08 09/11/08 09/21/08 10/01/08 10/11/08
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LUEchl
pri
In situ canopy and leaf measurement dates
PRI= (531-570)/(531+570)
LUEchl and PRI: LUEchl and PRI: in situin situ ASD canopy measurements ASD canopy measurements
y = 23.969x + 1.8647
R2 = 0.8306
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PRI
LU
Ech
l (g m
ol-
1)LUEchl vs. PRIy = 23.97x + 1.86
r2 = 0.83
Triangles over Circles are for the 5 days having both ASD and Hyperion images (2008 DOY 172, 190, 195, 231, 277). Hyperion data: 30 m, 10 nm bands.
USDA/ OPE3 Corn FieldUSDA/ OPE3 Corn FieldCompare LUEchl vs. PRI: Hyperion Compare LUEchl vs. PRI: Hyperion [[▲▲] ] and and in situin situ ASD measurements [ ASD measurements [ ] ]
30 m, 10 nm bands Hyperion = ▲
PRI= (531-570)/(531+570)
Product Prototyping for HyspIRIComparisons of GEP from various algorithms
60m simulated MOD17
MOD17 1km GPP60m HyperionPRI & fAPARchl
0 4.85gCm-2d-1
0 14.34gCm-2d-1
0 6.74gCm-2d-1
60m HyperionRGB Cheng et al. 2011. HyspIRI Symposium
Product Prototyping for HyspIRIComparisons of GEP from various algorithms
60m simulated MOD17
MOD17 1km GPP60m HyperionPRI & fAPARchl
0 14.34gCm-2d-1
0 6.74gCm-2d-1
60m HyperionRGB
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OPE3 flux tower
PRI fAPARchl
MOD17 mockup
MOD17 GPP
GEP
(gC
m-2
d-1
)
Cheng et al. 2011. HyspIRI Symposium
0 4.85gCm-2d-1
y = -29.291x + 7.1335
R2 = 0.7647
-0.50
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PRI (488)
LU
Ech
l (g
mo
l-1)
USDA/ Beltsville FieldUSDA/ Beltsville Field
MAIAC-MODIS fAPARchl and PRI (488)
y = -19.411x + 7.5556
R2 = 0.7841
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PRI(488)
LU
Ec
hl (
g M
J-1
)MODIS based fAPARchl and PRI (488)
@ Great White Mountain flux tower site, China
Scaling Light Use Efficiency in Arctic Tundra
From plot to region - Plot level LUE
Chamber measurements of photosynthesis of pure patches of vascular plants, mosses, and lichens
Spectral reflectance collected and convolved to Hyperion bands
All observations from late July and early August near Barrow, AK
- near peak of growing season
Data salvaged from old field work
R = Reflectance at 834 nmG = Reflectance at 671 nmB = Reflectance at 549 nm
R = Vascular Plant CoverG = Moss CoverB = Lichen CoverScale from 0 – 100%
Light Use Efficiency(x10,000)Based on coverage
Hyperion - Reflectance, Functional Type Cover, and LUE Day 201, 2009, Image subset around Barrow, AK Field measurements scaled to region find a 5-fold variation in LUE
Estimating Fluxes from MODIS Ocean BandsEstimating Fluxes from MODIS Ocean Bands in Canadian Forests in Canadian Forests
Examine Relationship between GEP and PRI*APAR from MODIS- Mid-growing season data for 6 different forest types- Fluxes from flux tower for time of overpass- Distinct differences in responses among sites
Remote Sensing of Fluxes: Hyperion and FluxnetRemote Sensing of Fluxes: Hyperion and Fluxnet
Can a single algorithm driven by hyperspectral satellite data provide an estimate of carbon flux variables over a wide range of sites?
Method: Matched flux data from LaThuile Fluxnet Synthesis with Hyperion imagery
Standardized flux calculation for all sites
18
La Thuile Flux Sites
CEOS Calibration Sites
Time Series at Flux Sites
80 observations of 33 different flux tower sites
Data from 2001 to 2007 Observed during mid-growing season
Multiple vegetation types
CO2 Flux Data Processing• Net Ecosystem Production (NEP, µmol m-2 s-1) is
the CO2 absorbed by the vegetation, measured by the flux tower.
• Ecosystem Respiration (Reco) was calculated from relationships developed between nighttime Net Ecosystem Exchange (NEE) and air temperature (sometimes, also soil moisture).
• Gross Ecosystem Production (GEP) is calculated from the observed NEE and Reco.
Multi-Site PRI and LUEMulti-Site PRI and LUE
CRO - CropsDBF - Deciduous Broadleaf ForestEBF - Evergreen Broadleaf ForestENF - Evergreen Needleleaf ForestOther - Wetland, Grassland, Mixed Forest, Closed Shrubland,Woody Savanna
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Multi-Site Multi-Site Vegetation IndexVegetation Index and LUE and LUE
• Best index (out of 107 tried) for overpass LUE was the first derivative at 732 nm divided by the derivative at 702 nm
79 Points
Multi-Site Vegetation Index and LUEMulti-Site Vegetation Index and LUE
• Best index (out of 107 tried) for both overpass and daily LUE was the first derivative at 732 nm divided by the derivative at 702 nm: D732/D702
N =79
At overpass time With daily fluxes
Stepwise Regression TestStepwise Regression Test
• Different input datasets chose different band sets for Daily LUE- 38 different bands chosen in 11 runs (10 subsets and all points together)- 9 runs chose band 732nm, 8 runs chose band 783nm
67 Points
• A wide range of bands can be used to produce good results (r > 0.82)
Stepwise Linear Regression - LUE Stepwise Linear Regression - LUE
Used Bands: R569, R732, R742, R2093, R2133, R2153, R2375 Used Bands: R518, R539, R549, R732, R783, R915, R1023
• Circled points are outliers. R and RMSE calculated with outliers removed
79 Points
Multi-Site Vegetation Index and RecoMulti-Site Vegetation Index and Reco• Best index (out of 107 tried) for Reco at overpass time was the
Normalized Difference Water Index (NDWI), using reflectances at 876 and 1245 nm. Reco = Ecosystem Respiration.
80 Points
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Partial Least Squares –LUE at OverpassPartial Least Squares –LUE at Overpass
79 Points
red - PLS Weighting Factorsblack - sample reflectance spectra
• An example of an approach that utilizes all of the spectral information
79 Points
red - PLS coefficientsblack - sample reflectance spectra
Partial Least Squares – Reco at OverpassPartial Least Squares – Reco at Overpass
• A common (global) spectral approach appears feasible. To derive it we need:– the capability of collecting hyperspectral observations of
globally-distributed sites representing a variety of vegetation types
– the ability to make repeated measurements of each site– Hyperion on EO-1 can provide data for these studies
• The strongest relationships use continuous spectra, narrow wavelength bands, and/or derivative parameters
• Multiple algorithms and/or band combinations are effective
Results-Conclusions
EO-1 Hyperion: Three Ecosystem Studies
FLUX Site Name
Location Climate Vegetation
1. Mongu Zambia Temperate/ warm summer
Kalahari/ Miombo Woodland
2. Duke North Carolina USA
Temperate/ no dry season/ hot summer
Hardwood forest/ Loblolly pine
3. Konza Prairie Kansas USA
Cold/ no dry season/ hot summer
Grassland
1.
2.3.
MSO Sites
Mongu
Time SeriesTime Series
Mongu, ZambiaDOY
Bio-indicator Bands (nm) R2 [NEP (GEP)]
G32 R750, 700, 450 0.83 (0.81) NL
Dmax D max (650…750 nm) 0.77 (0.87) NL
Dmax / D704 D(690-730) 0.79 (0.80) NL
mND705 R750, 704, 450 0.75 (0.79) NL
RE1 Av. R 675…705 0.71 (0.56) NL
EVI R (NIR, Red, Blue) 0.73 (0.88) L
NDVI Av. R760-900, R620-690 0.52 (0.60) NL
G32, Associated with Chlorophyll
(Gitelson et al. 2003)
The spectral bio-indicator associated with chlorophyll content (G32, green line) best captured the CO2 dynamics related to vegetation phenology.
Hyperion Spectral Indices and GEP at Mongu
DOY
A. Dry season (DOY 214)
B. Wet season (DOY 22)
0 8
Mongu: Seasonal change in G32 & NEPA. Dry season (DOY 214)
B. Wet season (DOY 22)
Estimated NEP (μmol m-2 s-1)
0 12
G32
Duke, NC Loblolly Pine DOY
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634162180203290300
DOYMixed Hardwoods
Pine site
Hardwood site
Duke Forest : PRI4 & NEPA. Winter (DOY 34)
B. Summer (DOY 203) -3 3
NEP (μmol m-2 s-1)
0 28
PRI4
LP
HW
LP
HW
Index Bands (nm) R2 [NEP (GEP) LUE]
PRI1 531, 570 0.84 (0.73) L
PRI4 531, 670 0.75 (0.63) 0.73 L
DPI D 680, 710, 690 0.91 (0.44) NL
NDWI 870, 1240 0.76 (0.60) L
NDVI NIR, Red 0.19 (0.48) L
Loblolly Pine (LP)
Hardwoods (HW)
Index Bands (nm) R2 [NEP (GEP) LUE]
PRI4 531, 670 0.84 (0.48) NL
Dmax D max (650…750 nm) 0.83 (0.40) NL
NDII 820, 1650 0.79 (0.34) L
EVI NIR, Red, Blue 0.84 (0.41) L
NDVI NIR, Red 0.63 (0.19) L
Bio- indicators of Photosynthetic Function
Derivative MaximumKonza (K), Mongu (M), Duke (D)
Normalized Difference Water Index Konza (K), Mongu (M), Duke (D)
y = -0.0002x2 + 0.0119x - 0.1395R² = 0.74
-0.25
-0.20
-0.15
-0.10
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ND
WI
NEP
Mongu
Duke
Konza
All Towers: Midday GEP vs. APAR
y = 0.0106x + 1.728R² = 0.85
y = 0.0166x - 0.2254R² = 0.76
y = 0.0428x - 11.256R² = 0.92
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Mid
day
GEP
(µm
ol m
-2s-1
)
Midday APAR (µmol m-2 s-1)
Mongu
Duke
Konza
Av. LUE = 0.011 mol/mol
Av. LUE = 0.017 mol/mol
Av. LUE = 0.043 mol/mol
Multiple Flux SitesKonza (K), Mongu (M), Duke (D)
Multiple Flux SitesKonza (K), Mongu (M), Duke (D)
8
0
Mongu: Seasonal change in G32 & NEPA
. Dry
se
aso
n (
DO
Y
214
)B
. Wet
sea
so
n (
DO
Y
22)
NEP (μmol m-2 s-1)
G32FCC (760, 650, 550 nm) 0 12
Duke Forest : PRI4 & NEPA
. Win
ter
(DO
Y
34)
B. S
um
me
r (D
OY
2
03)
PRI4
LP
HW
LP
HW
3
-3
NEP (μmol m-2 s-1)
FCC (760, 650, 550 nm)
0 28
EO-1 Hyperion Spectral Bio-Indicator of GEP/NEPEO-1 Hyperion Spectral Bio-Indicator of GEP/NEPBest Correlation to COBest Correlation to CO22 Uptake for Multiple Flux Sites Uptake for Multiple Flux Sites
Campbell et al. 20122012 William Nordberg Award
(6/8/2012)44
† NEP – net ecosystem production, GEP – gross ecosystem production‡ L – linear, NL – non-linear
• In 3 vastly different ecosystems, continuous reflectance data and a variety of spectral parameters, were correlated well to CO2 flux parameters (e.g. NEP, GEE, etc.). Imaging spectrometry provides spatial distribution maps of CO2 fluxes absorbed by the vegetation.
• The bio-indicators with strongest relationships were calculated using continuous spectra, using numerous wavelengths associated with chlorophyll content and/or derivative parameters.
• Common (global) spectral approach to trace vegetation function and estimate it’s CO2 sequestration ability is feasible. It requires:– a diverse spectral coverage, representative of the major ecosystem types, – spectral time series, to cover the dynamics within a cover type.
Findings
Remote Sensing of FluxesHyperion and Flux Towers
• Hyperion on EO-1 provides us with two important capabilities:– the capability of collecting hyperspectral observations of globally-
distributed sites, and – the ability to make repeated measurements of a site
• Provides a dataset for testing and developing algorithms for global data products
• The strongest relationships with carbon uptake parameters used continuous spectra, numerous wavelengths associated with chlorophyll content, and/or derivative parameters.
• A common (global) spectral approach appears feasible. To derive it will require:– Diverse coverage, representing major ecosystem types, and– time series, to cover the dynamics within a cover type.
RecommendationsThese studies utilize data from the existing flux
tower networkFor many HyspIRI products we will need more
studies applying algorithms for a number of different landcover types
- Use ground, aircraft, and satellite spectral reflectance data
- Need to develop protocols for ground measurements of potential HyspIRI products
- Need to establish network of sites measuring these products
- These sites can grow into a HyspIRI cal/val network
EO-1 Future Plan• Present Matsu Compute Cloud functionality
– Hyperion and ALI Level 1R processing– Hyperion and ALI Level 1 G processing– Web Coverage Processing Service (WCPS) – web service to rapidly create and
execute new algorithms for ALI and Hyperion data and includes:• Atmospheric Correction• ALI Pan Sharpening• Flood water classifier for ALI
– Namibia Flood Dashboard (mashup of ground and multiple satellite data and data products for floods)
• Augment the Matsu Cloud- Automated co-registration of Hyperion (depending on funding availability)
-Tile cutouts for Hyperion
• Lunar Calibration Schemes
• Intelligent Payload Module- High speed onboard processing for low latency products (target HyspIRI)
- Hyperion L0, L2 to emulate future HyspIRI data- WCPS - upload algorithms in realtime to customize processing of EO-1 like data- Core Flight Executive (cFE)- CASPER – onboard planner used on EO-1 is part of testbed
Future Directions• Expand the tests over additional ecosystem types (rain forest, temperate and
sub-arctic vegetation types); • Test additional spectral approaches (e. g. feature depth analysis)
• Special Issue of IEEE JSTARS on EO-1 (Guest Ed., E.M. Middleton), early Special Issue of IEEE JSTARS on EO-1 (Guest Ed., E.M. Middleton), early 2014.2014.
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