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4th Global Veg Workshop, June 16-19, 2009
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Development of Vegetation Development of Vegetation Products for U.S. GOES-R Products for U.S. GOES-R
Satellite MissionSatellite Mission
Yunyue Yu, Mitch Goldberg, Yunyue Yu, Mitch Goldberg, NOAA/NESDIS/StARNOAA/NESDIS/StAR
Peter Romanov, Peter Romanov, University of MarylandUniversity of Maryland
Hui Xu, Yuhong Tian, Hui Xu, Yuhong Tian, I. M. Systems Group, Inc.I. M. Systems Group, Inc.
4th Global Veg Workshop, June 16-19, 2009
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OutlinesOutlines
GOES-R Mission
AWG Land Team
Vegetation Products
Accomplishments
Summary
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GOES-R MissionGOES-R Mission
135 W 75 W
US GOES Imager CoverageUS GOES Imager Coverage
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GOES-R OverviewGOES-R Overview
GOES Satellite Mission National operational environmental observations for 24-hour of weather
and Earth’s environment
The GOES-R system provides an observation system that produces reliable data on atmosphere, terrestrial, fresh water, and ocean ecosystems data and will be one of the primary U.S. systems networked into the Global Observing System led by the World Meteorological Organization and that was set as a goal at the Earth Observation Summit (July 2003)
Support storm-scale weather forecasting and numerical modelers- To meet requirements, GOES continuously maintains operational satellites at
two locations (75º West and 135º West)
On-orbit spare ready in case of failure- currently operational : GOES-10 (K), -11(L), -12(M), -13(N)- Under Development: GOES-O, -P (N series)- GOES-R Series is the follow-on continuity program to GOES-N Series
(New Generation GOES Satellites)
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GOES-R MissionGOES-R Mission
GOES-R AWG program is a government-led effort to
broker algorithms between government, academic and broker algorithms between government, academic and commercial sources; commercial sources;
support the prototyping and demonstration of algorithm support the prototyping and demonstration of algorithm performance including algorithm/product metadata generation performance including algorithm/product metadata generation techniques, standards, and formats;techniques, standards, and formats;
provide algorithm software, test data sets, and benchmarks as provide algorithm software, test data sets, and benchmarks as potential solutions for the product generation functions, and potential solutions for the product generation functions, and
review and assess applicable GOES Incident Reports (GIRs).review and assess applicable GOES Incident Reports (GIRs).
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ABI Sensor ABI Sensor -- Scan Mode-- Scan Mode
There are two anticipated scan modes for the ABI:- Full disk images every 15 minutes + 5 min CONUS images +
mesoscale - Or, Full disk every 5 minutes
ABI scans about 5 times fasterthan the current GOES imager
Full Disk
N. Hemisphere
CONUS
Mesoscale
“Franklin”
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ABI SensorABI SensorChannelsChannels
Future GOESImager (ABI)Band
NominalWavelength
Range(μm)
Nominal CentralWavelength
(μm)
Nominal CentralWavenumber
(cm-1)
Nominal sub-satellite
IGFOV(km)
Sample Use
1 0.45-0.49 0.47 21277 1 Daytime aerosol-over-land
2 0.59-0.69 0.64 15625 0.5 Daytime clouds fog, insolation, wind
3 0.846-0.885 0.865 11561 1Daytime vegetation/burn scar & aerosol-
over-water, winds4 1.371-1.386 1.378 7257 2 Daytime cirrus cloud
5 1.58-1.64 1.61 6211 1Daytime cloud-top phase and particle
size, snow
6 2.225 - 2.275 2.25 4444 2Daytime land/cloud, particle size,
vegetation, snow
7 3.80-4.00 3.90 2564 2 Surface & cloud, fog at night, fire, winds
8 5.77-6.6 6.19 1616 2High-level atmospheric water vapor,
winds, rainfall
9 6.75-7.15 6.95 1439 2Mid-level atmospheric water vapor,
winds, rainfall
10 7.24-7.44 7.34 1362 2 Lower-level water vapor, winds & SO2
11 8.3-8.7 8.5 1176 2Total water for stability, cloud phase,
dust, SO2 rainfall12 9.42-9.8 9.61 1041 2 Total ozone, turbulence, and winds13 10.1-10.6 10.35 966 2 Surface & cloud
14 10.8-11.6 11.2 893 2 Imagery, ST, clouds, rainfall
15 11.8-12.8 12.3 813 2 Total water, ash, and ST
16 13.0-13.6 13.3 752 2Air temperature, cloud heights and
amounts
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GOES-R OverviewGOES-R Overview
GOES-R Algorithm Working Group
Product(s) Team NOAA Collaborator (Team Chair)
Air Quality/Aerosol Shobha Kondragunta (NOAA/STAR)
Aviation Ken. L. Pryor (NOAA/STAR)
Calibration/Validation Changyong Cao (NOAA/STAR)
Clouds Andrew Heidinger (NOAA/STAR)
Cryosphere Don Cline (NOAA/NWS)
Hydrology Bob Kuligosky (NOAA/STAR)
Land Surface Yunyue Yu (NOAA/STAR)
Ocean Color Menghua Wang (NOAA/STAR)
Sea Surface Temperature Alexander Ignatov (NOAA/STAR)
Proxy Data Fuzhong Weng (NOAA/STAR)
Radiation Budget Istvan Laszlo (NOAA/STAR)
Soundings Chris Barnet (NOAA/STAR)
Space Weather Steven Hill (NOAA/SEC)
Winds Jaime Daniels (NOAA/STAR)
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AWG Land Team and AWG Land Team and ProductsProducts
Normalized Difference Vegetation Normalized Difference Vegetation
IndexIndex Peter Romanov (Lead)Peter Romanov (Lead) Hui XuHui Xu Dan TarpleyDan Tarpley Bob YuBob Yu Kevin GalloKevin Gallo Felix KoganFelix Kogan Wei GuoWei Guo
Land Surface TemperatureLand Surface Temperature Bob Yu (Lead)Bob Yu (Lead) Dan TarpleyDan Tarpley Hui XuHui Xu Ming ChenMing Chen Konstantin VinnikovKonstantin Vinnikov Kevin GalloKevin Gallo
Software DevelopmentSoftware Development Hui Xu (Lead)Hui Xu (Lead) Shuang QiuShuang Qiu Ming ChenMing Chen Wei GuoWei Guo
Fire/Hot Spot CharacterizationFire/Hot Spot Characterization Chris Schmidt (Lead)Chris Schmidt (Lead) Ivan CsiszerIvan Csiszer Wilfrid SchroederWilfrid Schroeder Bob YuBob Yu Hui XuHui Xu
Clear Sky RadianceClear Sky Radiance** Peter Romanov (Lead)Peter Romanov (Lead) Hui XuHui Xu Dan TarpleyDan Tarpley
AWG Land Team Chair : Yunyue (Bob) YuAWG Land Team Chair : Yunyue (Bob) Yu
Flood/Standing WaterFlood/Standing Water Donglian Sun (Lead)Donglian Sun (Lead) Bob YuBob Yu Sanmei LiSanmei Li Hui XuHui Xu
Albedo/ReflectanceAlbedo/Reflectance Shunlin Liang (Lead)Shunlin Liang (Lead) Kaicun WangKaicun Wang Bob YuBob Yu Tao HeTao He Hongyi WuHongyi Wu
Green Vegetation FractionGreen Vegetation Fraction Peter Romanov (Lead)Peter Romanov (Lead) Felix Kogan (co-Lead)Felix Kogan (co-Lead) Yuhong Tian (co-Lead)Yuhong Tian (co-Lead) Bob YuBob Yu Dan TarpleyDan Tarpley Hui XuHui Xu
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Product PriorityProduct Priority
•Release 1 and 2 required for GOES-R launch readiness•Products are grouped into four Product Generation capability releases; releases 3 & 4 are options•Prioritization methodology: user prioritization, product precedence trees (MIT/LL, STAR), heavy hitters from latency requirements, and LIRD/MRD Prioritization Tiers
03/2009
03/2012
03/2011
03/2010
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GOES-R NDVI and GVF GOES-R NDVI and GVF SchedulesSchedules
NDVI GVF
Algorithm Design Review 12/21/2006 6/9/2009
Algorithm development 10/2006 – 04/2010 03/2009 – 06/2011
Critical Design Review 5/8/2008 1/28/2010
ATBD draft 6/27/2008 8/15/2008
Test Plan Review 3/23/2009 9/8/2010
Validation Plan (draft) 3/31/2009
ATBD (80% readiness) 6/26/2009 8/23/2010
ATBD (100% readiness) 6/21/2010 8/5/2011
Software Package ready 4/21/2010 6/13/2011
Development Schedule for GOES-R Veg Products
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NDVI CDR Requirements NDVI CDR Requirements
Nam
e
User &
Priority
Geographic
Coverage
(G, H
, C, M
)
Vertical
Res.
Horiz.
Res.
Mapping
Accuracy
Msm
nt.
Range
Msm
nt.
Accuracy
Refresh
Rate/C
overage Tim
e Option (M
ode 3) R
efresh Rate O
ption (Mode 4)
VA
GL
Product M
easurement P
recision
Tem
poral Coverage Q
ualifiers
Product E
xtent Qualifier
Cloud C
over Conditions Q
ualifier
Product Statistics Q
ualifier
Vegetation Vegetation Index Index
GOES-RGOES-R CC N/AN/A 2 2 kmkm
1 km1 km 0 to 1 0 to 1 (NDVI (NDVI units)units)
0.04 0.04 NDVI NDVI unitsunits
60 min60 min 60 min60 min 3236 3236 secsec
0.04 0.04 NDVI NDVI unitsunits
Sun at Sun at 67 67 degree degree solar solar zenith zenith angleangle
QuantitativQuantitative out to at e out to at least 55 least 55 degrees degrees LZA and LZA and qualitative qualitative beyondbeyond
Clear Clear ConditionConditions s associated associated with with threshold threshold accuracyaccuracy
Over Over specified specified geographgeographic areaic area
C – CONUS
Full Disk: Request for change of requirement has been submittedFull Disk: Request for change of requirement has been submitted
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GVF ADR RequirementsGVF ADR Requirements
Nam
e
User &
Priority
Geographic
Coverage
(G, H
, C, M
)
Vertical
Resolution
Horizontal
Resolution
Mapping
Accuracy
Measurem
ents
Range
Measurem
ents
Accuracy
Refresh
Rate/C
overage Tim
e Option (M
ode 3) R
efresh Rate O
ption (Mode 4)
Data
Latency
Product M
easurement P
recision
Tem
poral Coverage Q
ualifiers
Product E
xtent Qualifier
Cloud C
over Conditions Q
ualifier
Product Statistics Q
ualifier
Vegetation Fraction: Green
GOES-R C N/A 2 km 2 km 0.0 to 1 (unitless)
0.02 60 min 60 min 3236 sec 0.005 Sun at 67 degree daytime solar zenith angle
Quantitative out to at least 55 degrees LZA and qualitative beyond
Clear conditions associated with threshold accuracy
Over specified geographic area
C – CONUS
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NDVI AlgorithmNDVI Algorithm
NDVI = ( Rnir - Rvis ) / ( Rnir + Rvis )
Rnir: Near IR reflectance (0.86 µm, ch.3) Rvis: Visible reflectance (0.64 µm, ch.2)
Both reflectances are top of the atmosphere values
First introduced by Rouse, et al (1973)
Widely used in vegetation/climate monitoring since then, e.g., Tucker (1979), Holben, (1986), Myneni et al. (1997), Kogan (2001)
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NDVI Algorithm Flowchart NDVI Algorithm Flowchart and Input/outputand Input/output
Filename Format Contents
ABI_[DayTimeID]_NVI binary Scaled NDVI (NDVI+1)*100
ABI_[DayTimeID]_QC binary QC flag – cloud, snow, etc
High Level Flowchart of NDVI
End NDVI
NDVI start
Initialize variables/LUTs
Process ABI data
to calculate NDVI
Read ABI DataRead Ancillary Data
NDVI Data Formatting,
Metadata
Output of ABI NDVI product
Byte Bit Description
Ancillary Data Flags1 11 2 Day / Night flag1 31 4 Land / Ocean flag1 51 61 71 8 Snow / No snow flag2 1 (cloud) clear2 2 probably clear2 3 probably cloudy2 4 cloudy2 52 62 72 8
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SEVIRI as ABI ProxySEVIRI as ABI Proxy
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NDVI Algorithm OutputNDVI Algorithm Output-- SEVIRI as Proxy-- SEVIRI as Proxy
False color composite and the derived NDVI map of SEVIRI full disk image for April 9, 2008, 12:15 UTC
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Test Outputs – Instantaneous, NDVI Products
Presented at GOES-R 2008 Annual Conference, Madison, WI 53706, 23-26 June 2008.
The instantaneous NDVI taken at 12:15 UTC (below), and at 12:45 UTC (right)
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Test Outputs –Daily and Weekly NDVI Products
Presented at GOES-R 2008 Annual Conference, Madison, WI 53706, 23-26 June 2008.
The NDVI daily composite (June 15, 2008) (below), and weekly composite (June 9-15,
2008) (right)
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NDVI Test PlanNDVI Test Plan – Offline Validation: Test Results– Offline Validation: Test Results
Fraction of cases with over 0.05 NDVI diurnal changeFraction of cases with over 0.05 NDVI diurnal change
MSG SEVIRI full disk data, 1145UTCMSG SEVIRI full disk data, 1145UTC
0
10
20
30
40
50
60
70
80
90
100
2007 2007.5 2008 2008.5
year
Fra
ctio
n,
% All data
Simple cloud mask
Added dR1 threshold
“All data” : No cloud mask applied, Ɵsat < 700, Ɵsol < 700 “Simple cloud mask”: T9>285K, R1<60%, NDVI>0.05“Added dR1 threshold”: Cloud mask consisting of “Simple cloud mask” and 10% max daily
change in R1
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GVF BackgroundGVF Background
Green Vegetation Fraction (GVF): fraction of the area within the instrument footprint occupied by photosynthetically active green vegetation
1. Datasets based on observations from polar-orbiting satellite data
NESDIS/STAR Operational Monthly: Monthly 0.144-deg (~16km) global climatology of GVF from 5 years of AVHRR data. (Gutman & Ignatov, 1998).
NESDIS/STAR New Weekly GVF: 28 years (1982~present) AVHRR-based near real-time weekly global 0.144-deg (~16km) GVF (Jiang et al., 2009). Includes weekly GVF climatology.
2. Datasets based on observations from Geostationary satellite data
EUMETSAT/MSG SEVIRI Product: Daily since 2005, 3km resolution, covers land area within Meteosat domain.
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NDVI-based GVF Algorithm
GVF = (NDVImax - NDVI) / (NDVImax - NDVImin)
NDVI : Observed NDVI
NDVImax : Maximum NDVI corresponding to 100% vegetation cover
NDVImin : Minimum NDVI corresponding to 0% vegetation cover
NDVImax and NDVImin are global parameters independent of location or land cover type
GOES-R GVF AlgorithmGOES-R GVF Algorithm
GOES-R ABI is expected to characterize GVF at higher spatial and temporal resolution as compared to available AVHRR-based datasets
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GVF ChallengesGVF Challenges
1. Global NDVImax and NDVImin have to be established
2. NDVI angular anisotropy has to be accounted for
- NDVI, NDVImax and NDVImin should be brought to the same geometry of observations
- NDVI angular anisotropy is neglected In current GVF algorithms
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NDVI Angular AnisotropyNDVI Angular Anisotropy
Up to 0.4 change in NDVI due to the change in solar zenith angle
NDVI angular anisotropy is the strongest for scenes with high NDVI value
Examples of NDVI daily (July) change Examples of NDVI daily (July) change
MSG-SEVIRI cloud-clear dataMSG-SEVIRI cloud-clear data
Effect of solar zenith angleMixed ForestMixed Forest
Lat=47.48NLat=47.48N
Lon=13.98ELon=13.98E
CroplandCropland
Lat=48.15NLat=48.15N
Lon=33.10ELon=33.10E
Bare GroundBare Ground
Lat=24.96NLat=24.96N
Lon=0.25WLon=0.25W
MSG
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NDVI Angular Anisotropy NDVI Angular Anisotropy (2)(2)
Relative azimuth may cause about 0.1 change in NDVI
Changing relative azimuth may cause asymmetry of NDVI daily change
Effect of solar-satellite relative azimuth angle
4 8 12 16 20L o ca l Tim e , h
0
10
20
30
40
50
60
70
ND
VI*
10
0
0
20
40
60
80
100
120
140
160
180A
ng
le, d
eg
N D V I
R elative Azim uth
Solar E levation
Y ear: 2007, D ay: 78Location : 16 .2 0S, 51.6 0WS atellite v iew ang le: 60 0
Backscatter Forward Scatter
Lat=16.2SLon=51.6W
4 8 12 16 20L o ca l Tim e , h
0
10
20
30
40
50
60
70
ND
VI*
10
0
0
20
40
60
80
100
120
140
160
180
An
gle
, de
g
N D V I
R elative Azim uth
Solar E levation
Y ear: 2007, D ay: 302Location : 8 .7 0N , 22.7 0ES ate llite v iew ang le: 28 0
BackscatterForward Scatter
Lat=8.7NLon=22.7E
4 8 12 16 20L o ca l Tim e , h
0
10
20
30
40
50
60
70
ND
VI*
10
0
0
20
40
60
80
100
120
140
160
180
An
gle
, de
g
N D V I
R elative Azim uth
Solar E levation
Y ear: 2007, D ay: 183Location: 42.7 0N , 3 .6 0WS atellite v iew ang le: 49 0
BackscatterFS FS
Lat=42.7NLon=3.6W
MSG
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NDVI Angular Model NDVI Angular Model
Empirical approach
Follow techniques for angular correction of reflectance to develop NDVI angular model
((Roujean et al., Roujean et al., 1992; 1992; Wanner et al.,Wanner et al., 1999; 1999; Lucht et al., Lucht et al., 2000)2000)
Generate a comprehensive dataset of clear-sky observations
Test different analytical parameterizations for NDVI anisotropy - Solar-satellite angle reciprocity will be used
Determine the parameterization providing the best characterization of the observed NDVI daily change
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NDVINDVIminmin and NDVI and NDVImaxmax
NDVImax and NDVImin defined after Sellers et al. (1996)
NDVImax = 5th upper percentile of global NDVI values corrected for angular anisotropy
NDVImin = 5th lower percentile of global NDVI values after cloud filtering
We expect that processing of 1-2 month of SEVIRI data for two seasons would be enough to make reliable estimates of NDVImax and
NDVImin
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Algorithm Development Algorithm Development Strategy Strategy
1. Adopt NDVI-based GVF formulation
2. Use MSG SEVIRI as GOES-R prototype
3. Develop an analytical model to correct NDVI for angular anisotropy
4. Determine global NDVImax and NDVImin
5. Apply the new GVF algorithm to MSG SEVIRI data
6. Evaluate the algorithm performance
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GVF Algorithm Processing GVF Algorithm Processing OutlineOutline
Acquire satellite data (NDVI, angle values)
Acquire model data (NDVImin, NDVImax,
NDVI angular model parameters)
Acquire ancillary data (cloud mask, land/water mask)
Apply cloud mask, water mask
Calculate GVF
GVF start
GVF end
Declare & Initialize variables
Read Data
Write files
Bring NDVI to the reference geometry
Flow Chart of GVF
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SummarySummary
• GOES-R NDVI will be calculated from TOA reflectances
• GOES-R GVF will be derived from NDVI product after angular correction
• Empirical approach will be used in the development of NDVI angular correction algorithm and in estimating NDVImax and NDVImin
• TTailored products such as daily and weekly products and products such as daily and weekly products and mostly cloud-free NDVI and GVF products will be developedmostly cloud-free NDVI and GVF products will be developed