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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600

-50 0 50 100 150 200 250 300 350 400 450 500 550 600

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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450 700 950 1200 1450 1700 1950 2200 2450

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

0

0.5

1

1.5

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2.5

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

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1.52

2.53

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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

21

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

26

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

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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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day

GEP

(µm

ol m

-2s-1

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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

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-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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