Getis Gi statistics, calculated using ENVI for a moving 3x3 windows (9 pixels, ~ 100 m), to permit visualization of the homogeneous regions. A cluster of pixels with high digital counts is indicated by largely positive Gi* values, while a cluster of pixels with low digital counts is indicated by largely negative Gi* values.
Cornfield: 0.61 0.29 0.30 0.34 0.82 0.74 Surrounding forest: 0.50 0.86 0.79 0.78 0.79 0.85 The cornfield site: we have 6 Hyperion images in 2008 from Spring, to Summer, to Fall. The first row are true color images. The second row are the fAPARcanopy images based on an empirical relationship between fAPARcanopy and NDVI, which is widely used. The cornfield is surrounded by forests. Corn was planted before day of year 195. The corn fAPARcanopy was saturated during its peak of growing season. For the surrounding forest, its fAPARcanopy saturated from late spring to summer.
Cornfield: 0.51 0.13 0.16 0.23 0.77 0.69 0.40 0.10 0.12 0.18 0.71 0.46 0.11 0.03 0.04 0.04 0.05 0.23 Surrounding forest: 0.22 0.82 0.80 0.80 0.80 0.71 0.15 0.62 0.51 0.50 0.48 0.50 0.06 0.20 0.29 0.30 0.32 0.21 Four rows: fAPARcanopy, fAPARleaf, fAPARchl and fAPARNPV. You can see how different these 3 fAPARs are from fAPARcanopy. fAPARleaf also has some saturation issue. The cornfield and the surrounding forest have distinct fAPARchl and fAPARNPV seasonality. Before corn was planted, both fAPARchl and fAPARNPV were low. Corn was the greenest on day 231 and fAPARNPV was low. During the senescence period, fAPARchl decreased and fAPARNPV increased. The forest was the greenest on day 172. after that, fAPARchl gradually decreased and fAPARNPV increased. fAPARNPV on days 190 -231 kept same.
For the MODIS observations over the forest site, here is the comparison between Light Use efficiency at chlorphyll level and the narrow band MODIS PRI. The R2 is 0.78, very nice correlation.
This raises issues that we will explain using the leaf/canopy models
R dynamics at Mongu are a result of biophysical stress caused by the seasonal rainfall, which is reinforced by the CO 2 flux.
Transcript of "EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION AND FUTURE PLANS"
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION AND FUTURE PLANS Elizabeth M. Middleton NASA/Goddard Space Flight Center, USAPetya K. E. Campbell1, K. Fred Huemmrich1, Qingyuan Zhang2, Yen-Ben Cheng3, David Landis4, Stephen Ungar2, Lawrence Ong5, and Nathan Pollack5 1 University of Maryland Baltimore County 2 Universities Space Research Association (USRA) 3 Earth Resources Technology, Inc. 4 Sigma Space Corp. 5 Science Systems and Applications, Inc. IGARSS’12 MO3.3 Spaceborne Imaging Spectroscopy Missions: Updates, and Global Datasets and Products [#4254] Munich, 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
N A. B. 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 + + N 4 km -1 -0.5 0 0.5 +1 Railroad Valley Playa site (cross): A. Natural color composite (RGB: 651,549,447), B. Getis Gi statistics, displaying the homogeneous regions Mean reflectance spectra (solid line)Campbell et al. 2012 Standard deviations (dashed blue line)
EO-1 Hyperion Image Processing Level 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. 700 ice AC ice AT 600 bright target AC bright target AT corn (r = 0.95) 550 600 corn AC corn AT forest (r = 0.98) lichens AC lichens AT 500 water (r = 0.92) forest AC fores AT 450 500 bright target (r = 0.97) water AC water AT 400 lichens (r = 0.98) 350 ice (r = 0.99) 400 300 300 250 O N A R C 200%naR elct)(f 200 150 100 100 50 0 0 -50 450 700 950 1200 1450 1700 1950 2200 2450 -50 0 50 100 150 200 250 300 350 400 450 500 550 600 Wavelength (nm) ATREM
Scaling Fluxes to AircraftImagery of cornfield from Airborne Imaging Spectrometer for Applications(AISA) data collected on September 14, 2009. Left panel shows fAPAR fromNDVI; middle panel is PRI; and right panel is modeled GEP in mg CO2 m-2s-1 using the model derived from ground reflectance data.
USDA Cornfield site 2008EO-1 Hyperion True color fAPARcanopy DOY 108 172 190 195 231 277 2008 Spring Summer Fall
fAPARcanopy fAPARleaf fAPARchl fAPARNPV DOY 108 172 190 195 231 277 2008 Spring Summer Fall
2.5 LUEchl and PRI: in situ ASD canopy measurements 0.03 0.02 2 PRI= (ρ 531-ρ 570)/(ρ 531+ρ 570) 0.01 01.5 -0.01 LUEchl -0.02 pri 1 -0.03 -0.040.5 -0.05 In situ canopy and leaf measurement dates -0.06 0 -0.0707/13/08 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 2.5 2 y = 23.969x + 1.8647 LUEchl(g mol-1) 2 LUEchl vs. PRI R = 0.8306 1.5 y = 23.97x + 1.86 1 r2 = 0.83 0.5 0 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 PRI
USDA/ OPE3 Corn Field Compare LUEchl vs. PRI: Hyperion [▲] and in situ ASD measurements [ ] PRI= (ρ 531-ρ 570)/(ρ 531+ρ 570) 30 m, 10 nm bands Hyperion = ▲Triangles over Circles are for the 5 days having both ASD and Hyperion images (2008DOY 172, 190, 195, 231, 277). Hyperion data: 30 m, 10 nm bands.
Product Prototyping for HyspIRI Comparisons of GEP from various algorithms 0 14.34 0 6.74 0 4.85 gCm-2d-1 gCm-2d-1 gCm-2d-160m Hyperion 60m Hyperion 60m simulated MOD17 1km GPP RGB PRI & fAPARchl MOD17 Cheng et al. 2011. HyspIRI Symposium
Product Prototyping for HyspIRI Comparisons of GEP from various algorithms 12 10 -2 -1 8 d ) 6 4 m G P C E g ( 2 0 OPE3 flux PRI MOD17 MOD17 tower fAPARchl mockup GPP 0 14.34 0 6.74 0 4.85 gCm-2d-1 gCm-2d-1 gCm-2d-160m Hyperion 60m Hyperion 60m simulated MOD17 1km GPP RGB PRI & fAPARchl MOD17 Cheng et al. 2011. HyspIRI Symposium
USDA/ Beltsville Field MAIAC-MODIS fAPARchl and PRI (488) 4.5LUEchl (g mol-1) 4 y = -29.291x + 7.1335 3.5 3 R2 = 0.7647 2.5 2 1.5 1 0.5 0 -0.5 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 PRI (488)
MODIS based fAPARchl and PRI (488) @ Great White Mountain flux tower site, China 8 y = -19.411x + 7.5556 7 R2 = 0.7841 6LUEchl (g MJ-1) 5 4 3 2 1 0 0 0.1 0.2 0.3 0.4 PRI(488)
Scaling Light Use Efficiency in Arctic Tundra From plot to region - Plot level LUEChamber measurements of photosynthesis of pure patches of vascular plants, mosses, and lichensSpectral reflectance collected and convolved to Hyperion bandsAll observations from late July and early August near Barrow, AK- near peak of growing seasonData salvaged from old field work
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 R = Reflectance at 834 nm R = Vascular Plant Cover Light Use Efficiency G = Reflectance at 671 nm G = Moss Cover (x10,000) B = Reflectance at 549 nm B = Lichen Cover Based on coverage Scale from 0 – 100%
Estimating Fluxes from MODIS Ocean Bands in Canadian ForestsExamine 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 FluxnetCan 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 sites80 observations of 33 different flux tower sitesData from 2001 to 2007 Observed during mid-growing seasonMultiple vegetation types Time Series at Flux Sites La Thuile Flux Sites CEOS Calibration Sites 18
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 Vegetation Index 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 21
Multi-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 At overpass time With daily fluxes N =79
Stepwise Regression Test• A wide range of bands can be used to produce good results (r > 0.82)• 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
Stepwise Linear Regression - LUE • Circled points are outliers. R and RMSE calculated with outliers removed 79 PointsUsed Bands: R569, R732, R742, R2093, R2133, R2153, R2375 Used Bands: R518, R539, R549, R732, R783, R915, R1023
Multi-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
Partial Least Squares –LUE at Overpass• An example of an approach that utilizes all of the spectral informationred - PLS Weighting Factorsblack - sample reflectance spectra 79 Points 26
Partial Least Squares – Reco at Overpass red - PLS coefficients black - sample reflectance spectra 79 Points
Results-Conclusions• 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
EO-1 Hyperion: Three Ecosystem Studies Time SeriesFLUX Site Locatio Climate VegetationName1. Mongu n Zambia Temperate/ warm Kalahari/ summer Miombo Woodland2. Duke North Temperate/ no Hardwood Carolina dry season/ hot forest/ Loblolly USA summer pine3. Konza Prairie Kansas Cold/ no dry Grassland USA season/ hot summer Mongu 3. 2. 1. MSO Sites
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) NLDmax / 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)
Hyperion Spectral Indices and GEP at Mongu B. Wet season (DOY 22) A. Dry season (DOY 214) DOYThe spectral bio-indicator associated with chlorophyll content (G32, green line) best captured the CO2 dynamics related to vegetation phenology.
Mongu: Seasonal change in G32 & NEPA. Dry season (DOY 214) G32 Estimated NEP (μmol m-2 s-1) 0 8 0 12B. Wet season (DOY 22)
Duke, NC Loblolly Pine DOY Pine site 4000 DOY 3500 Mixed 6Hardwood site Hardwoods 34 162 3000 180 2500 203 290 2000 300 1500 1000 500 0 450 700 950 1200 1450 1700 1950 2200 2450
Bio- indicators of Photosynthetic FunctionLoblolly Pine (LP)Index Bands (nm) R2 [NEP (GEP) LUE]PRI1 531, 570 0.84 (0.73) LPRI4 531, 670 0.75 (0.63) 0.73 L DPI D 680, 710, 690 0.91 (0.44) NLNDWI 870, 1240 0.76 (0.60) LNDVI NIR, Red 0.19 (0.48) LHardwoods (HW)Index Bands (nm) R2 [NEP (GEP) LUE]PRI4 531, 670 0.84 (0.48) NLDmax D max (650…750 nm) 0.83 (0.40) NLNDII 820, 1650 0.79 (0.34) L EVI NIR, Red, Blue 0.84 (0.41) LNDVI NIR, Red 0.63 (0.19) L
Derivative Maximum Konza (K), Mongu (M), Duke (D)
Normalized Difference Water Index Konza (K), Mongu (M), Duke (D) 0.10 0.05 0.00 -0.05 Mongu -0.10 Duke W D N I -0.15 Konza -0.20 y = -0.0002x2 + 0.0119x - 0.1395 R² = 0.74 -0.25 -5 0 5 10 15 20 25 30 35 40 NEP
All Towers: Midday GEP vs. APAR 75 Mongu Av. LUE = 0.011 mol/mol 65 Duke Av. LUE = 0.017 mol/mol )-1 s 55 Konza Av. LUE = 0.043 mol/mol-2 45 y = 0.0166x - 0.2254 R² = 0.76 35 m µ o ( l y = 0.0428x - 11.256 25 R² = 0.92 15 M G d P a E y y = 0.0106x + 1.728 i 5 R² = 0.85 -5 0 500 1000 1500 2000 Midday APAR (µmol m-2 s-1)
Multiple Flux Sites Konza (K), Mongu (M), Duke (D)
Multiple Flux Sites Konza (K), Mongu (M), Duke (D)
A. Dry season (DOY214) Mongu: Seasonal change in G32 & NEP 8 FCC (760, 650, 550 nm) G32 NEP (μmol m-2 0 12 s ) -1B. Wet season (DOY 22) 0
EO-1 Hyperion Spectral Bio-Indicator of GEP/NEPBest Correlation to CO2 Uptake for Multiple Flux Sites † NEP – net ecosystem production, GEP – gross ecosystem production ‡ L – linear, NL – non-linear Campbell et al. 2012 2012 William Nordberg Award 44
Findings• 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.
Remote Sensing of Fluxes Hyperion 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 2014. ? ? ? ? ?
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