radiometric and biophysical measures of global vegetation from multi-dimensional modis data...

Radiometric and biophysical measures of global vegetationfrom multi-dimensional MODIS data

Ramakrishna NemaniNTSG

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

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)

• 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

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

Spatio-temporal vegetation dynamics

1999 Onset of Greenness

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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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)

Global Leaf Area Index derived from Pathfinder NDVI and NDVI-LAI relationships

Global FPAR derived from Pathfinder NDVI and NDVI-FPAR relationships

Relating transpiration and photosynthesis to NDVI, 1988

Spectral reflectance of leaves

Theoretical basis for spectral vegetation indices:

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

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

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

Angular dependence

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.

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

Dotted lines indicate AVHRR bands

1

RED

2

NIR

Normalizing the VIs to nadir values

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)

MODIS-VI Compositing Scheme Flow Diagram

Global NDVI at 500 mDOY 113-128

500m NDVI subset DOY 113-128

Tapajós

MOD13A1 QA

500m

1km EVI Time Series

1km NDVI Time Series

South America

1 km VI’s Tapajós 113 - 128

‘Forest’

NDVI EVI

NDVI

EVI

MODIS & AVHRR NDVI Comparisons

Dotted lines indicate AVHRR bands

1

RED

2

NIR

AVHRR & MODIS Red and NIR bands

RED Reflectance (%)

NIR

Re

flect

an

ce (

%)

AVHRR

Soil Line

ResidualCloud Cover

White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water

R ED R eflectance (% )

NIR

Re

flect

an

ce (

%)

M ODIS

Soil L ine

White: Needle forestBlue : Broadleaf forestGreen: GrassPurple: CropYellow: ShrubRed : Water

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,

BIOPHYSICAL MEASURESLeaf Area Index (m2/m2):

FPAR (Fraction of absorbed PAR):

Incident Radiation

Ground

LeafLeaf

Leaf

LeafLeaf

LeafPARabsorption

(radiometric)

Leaf Area(structural)

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

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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NDVI

FPA

R

Leaf Area Index (LAI)

Fraction of intercepted photosynthetically active

radiation (FPAR)

Functional relations

R ED R eflectance (% )

NIR

Re

flect

an

ce (

%)

M ODIS

Soil L ine

0.70 NDVI

Need for a more robust approach

FPAR, LAIAlgorithmic Approach

• Two-tier algorithmic approach:

• LUT based approach using spectral as well as angular observations

• simple VI based backup

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

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

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

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

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

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)

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

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

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

THE LUT

Key features:

energy conservationability to ingest multiple wavelengthsallows the use of uncertainitiesangular data as a source of information

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

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

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

When does LUT approach fail?

Land cover mixtures

Effect of changing Epsilon

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FPA

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Leaf Area Index (LAI)

Fraction of intercepted photosynthetically active

radiation (FPAR)

SATURATION

Deriving LAI/FPAR at 250m resolution!

Need land cover at 250mBlue band is at 500m

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