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Radiometric and biophysical measures of global vegetationfrom multi-dimensional MODIS data
Ramakrishna NemaniNTSG
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Acknowledgements:
University of Arizona Boston University NTSGAlfredo Huete Ranga Myneni Joe GlassyKamel Didan Y. Knyazikhhin Petr VotavaTomoaki Miura Y. ZhgangHiroki Yoshioka Y. TianLaerte Ferreira Xiang Gao Karim Batchily
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Radiometric Measures Vegetation Indices
SR (Simple Ratio), MSR (Modified SR)
SAVI (Soil Adjusted VI), MSAVI, ARVI, GEMI
NDVI (Normalized Difference Vegetation Index)
EVI (Enhanced Vegetation Index)
Biophysical Measures
Leaf Area Index (Area of leaves per unit ground area, m2/m2)
FPAR (Fraction of incident PAR that is absorbed)
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• Vegetation Indices are ‘robust’ spectral transformations of two or more bands designed to enhance the ‘vegetation signal’ and allow for reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.
VEGETATION INDICES
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APPLICATIONS
Indicators of seasonal and inter-annual variations in vegetation (phenology)
Change detection studies (human/ climate) Tool for monitoring and mapping vegetation Serve as intermediaries is the assessment of
various biophysical parameters: leaf area index (LAI), % green cover, biomass, FPAR, land cover classification
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Spatio-temporal vegetation dynamics
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1999 Onset of Greenness
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Departure from Average Maps from the Wildland Fire Assessment System
Departure from Average maps relate current year vegetative greenness to average vegetative greenness for the same time of year.
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0
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
NDVI
LA
I
Grasslands and Cereal CropsMODIS MOD15 Back-up Algorithm
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NDVI
FPA
R
Leaf Area Index (LAI)
Fraction of intercepted photosynthetically active
radiation (FPAR)
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Global Leaf Area Index derived from Pathfinder NDVI and NDVI-LAI relationships
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Global FPAR derived from Pathfinder NDVI and NDVI-FPAR relationships
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Relating transpiration and photosynthesis to NDVI, 1988
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Spectral reflectance of leaves
Theoretical basis for spectral vegetation indices:
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SVI Formulations
Simple Ratio = NIR/Red
Normalized Difference = (NIR-Red)/(NIR+Red)Vegetation Index
Advantages: simple
Disadvantages: residual influences of atmosphere, background and viewing geometry
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Atmospheric Influences on Spectral Response Functions
Path Radiance
Sunlight
SkylightReflected Energy
Total Radiance
Atmosphere influences are not the same for Red and NIR
Water vapor absorptionScattering by aerosols
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Wavelength in Micrometers
TM4
Band 6 : 10.4 - 12.5
Reflectance
1.0 1.5 2.0
1 2 3 4 5 7
2.50 0.5
Background InfluencesBackground Influences
Vegetation
Dry Soil
Wet Soil
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Angular dependence
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VI Equations
• Enhanced Vegetation Index:
LCCG
blueredNIR
redNIR
21
EVI = EVI =
-where is atmospherically-corrected, surface reflectances, L is the canopy background adjustment, G is a gain factor, and C1 , C2 are coefficients for atmospheric resistance.
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MODIS Standard Vegetation Index Products
Products The MODIS Products include 2 Vegetation Indices
(NDVI, EVI) and QA produced at 16-day and monthly intervals at 250m/ 500m, 1km, and 25km resolutions
The narrower ‘red’ MODIS band provides increased chlorophyll sensitivity (band 1),
The narrower ‘NIR’ MODIS band avoids water vapor absorption (band 2)
Use of the blue channel in the EVI provides aerosol resistance
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Dotted lines indicate AVHRR bands
1
RED
2
NIR
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Normalizing the VIs to nadir values
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Compositing Algorithm Provide cloud-free VI product over set temporal
intervals, Reduce atmosphere variability & contamination Minimize BRDF effects due to view and sun angle
geometry variations Depict and reconstruct phenological variations Accurately discriminate inter-annual variations in
vegetation.Physical and semi-empirical BRDF models
Maximum VI (MVC) or constrained VI (CMVC)
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MODIS-VI Compositing Scheme Flow Diagram
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Global NDVI at 500 mDOY 113-128
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500m NDVI subset DOY 113-128
Tapajós
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MOD13A1 QA
500m
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1km EVI Time Series
1km NDVI Time Series
South America
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1 km VI’s Tapajós 113 - 128
‘Forest’
NDVI EVI
NDVI
EVI
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MODIS & AVHRR NDVI Comparisons
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Dotted lines indicate AVHRR bands
1
RED
2
NIR
AVHRR & MODIS Red and NIR bands
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RED Reflectance (%)
NIR
Re
flect
an
ce (
%)
AVHRR
Soil Line
ResidualCloud Cover
White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water
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SUMMARY• Both indices were robust and performed well in global
vegetation monitoring and analysis
• The improved spectral and spatial resolutions of MODIS offer the potential for improved change detection / land use and conversion studies,
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BIOPHYSICAL MEASURESLeaf Area Index (m2/m2):
FPAR (Fraction of absorbed PAR):
Incident Radiation
Ground
LeafLeaf
Leaf
LeafLeaf
LeafPARabsorption
(radiometric)
Leaf Area(structural)
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Applications of FPAR and LAI
• FPAR and LAI are useful variables which help describe:– canopy structure– radiation absorption– vegetative productivity– seasonal boundaries, phenological state– global carbon cycling
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MODIS Terrestrial Productivity
Remote SensingInputs
ModelLand Cover
FPAR
LAI
NPP = GPP - Respiration
Outputs
Weekly and
AnnualProductivity
Daily Weather(Tmin, Tmax, Rnet)
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0
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I
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NDVI
FPA
R
Leaf Area Index (LAI)
Fraction of intercepted photosynthetically active
radiation (FPAR)
Functional relations
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
0.70 NDVI
Need for a more robust approach
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FPAR, LAIAlgorithmic Approach
• Two-tier algorithmic approach:
• LUT based approach using spectral as well as angular observations
• simple VI based backup
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
Controlling factors:Leaf optical properties (refl,tran,abs)Canopy structureBackground reflectanceSun-sensor geometryLeaf area
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
Controlling factors:Leaf optical properties (refl,tran,abs)Canopy structureBackground reflectanceSun-sensor geometryLeaf area
White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water
0.70 NDVI
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
Controlling factors:Leaf optical properties (refl,tran,abs)Canopy structureBackground reflectanceSun-sensor geometryLeaf area
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The LUT contains entries at one critical wavelength only, and certain other non-wavelength dependent constants; thus, as the algorithm ingests 2 band data or 4 band data or even 7 band data, the size of the LUT is the same!
Leaf Spectral reflectance is characterized for 6 biomes at 152 points.
RSAC figure
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Wavelength in Micrometers
TM
MSS
4
5 6 7 8
Band 6 : 10.4 - 12.5
Reflectance
1.0 1.5 2.0
1 2 3 4 5 7
2.50 0.5
Vegetation
Jarosite
Kaolinite
Dry Soil
Wet Soil
Background parameterization (25 types)
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since the main algorithm is physically based, sun and view angle changes are treated as SOURCES of information rather than NOISE and thus aid in LAI/FPAR retrievals
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LAI is defined as:
LAI = g * LAIo
LAIo is mean LAI of a plant
g is canopy cover, which controls both total LAI as well as background contribution
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THE LUT
Contains:
for each biome (6)
leaf albedo at one wavelength coefficients to compute albedo any wavelength coefficients to compute BRF coefficients to compute effective background reflectance sun-sensor geometry intervals number of LAI intervals LAI saturation point
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THE LUT
Key features:
energy conservationability to ingest multiple wavelengthsallows the use of uncertainitiesangular data as a source of information
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FPAR, LAI Algorithm
• Inputs– Aggregated and atmospherically corrected 1km
surface reflectances from channels {1..6}, and their uncertainities; currently only 1,2 {VIS,NIR} are used.
– Land cover classification (IGBP translated to 6-class biome scheme; new 6-class coming.
– Ancillary data: Radiative Transfer model lookup tables, epsilon
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R ED R eflectance (% )
NIR
Re
flect
an
ce (
%)
M ODIS
Soil L ine
White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water
Controlling factors:
Background reflectanceSun-sensor geometryLeaf area
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FPAR, LAI AlgorithmOutputs
a distribution of LAI and FPAR, and NOT a single value!
The mean of the distribution and its standard deviation arereported, thus providing an error/uncertainity estimate of itsown.
LAI
Freq
uenc
y
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When does LUT approach fail?
Land cover mixtures
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Effect of changing Epsilon
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0
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NDVI
LA
I
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1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
NDVI
FPA
R
Leaf Area Index (LAI)
Fraction of intercepted photosynthetically active
radiation (FPAR)
SATURATION
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Deriving LAI/FPAR at 250m resolution!
Need land cover at 250mBlue band is at 500m
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SUMMARY
-physically based approach
-use of angular data (e.g. MISR synergism)
-realizing a distribution of LAIs rather than one LAI
-ability to change the LUT for other sensors
-VI based backup