4 th global veg workshop, june 16-19, 2009 1 development of vegetation products for u.s. goes-r...

4th Global Veg Workshop, June 16-19, 2009

1

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.


2

OutlinesOutlines

GOES-R Mission

AWG Land Team

Vegetation Products

Accomplishments

Summary


3

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


21

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


Lon=33.10ELon=33.10E

Bare GroundBare Ground


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


26

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


27

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


28

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


29

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


30

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

4 th global veg workshop, june 16-19, 2009 1 development of vegetation products for u.s. goes-r...

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