Yujie Wang Geography Department, Boston University

Assessment of the MODIS LAI and FPAR Algorithm: Retrieval Quality, Theoretical Basis and Validation

Yujie Wang

Geography Department, Boston University

Ph. D. dissertation Defense

Dissertation committee

Ranga B. MyneniYuri KnyazikhinMark A. Friedl

Curtis E. WoodcockJeffrey L. Privette 1 of 51

Summary of Presentation

1. Motivation

2. Investigation of Retrieval Quality as a function of Input and Model Uncertainty

3. Parameterization of the Algorithm in Light of the Law of Energy Conservation

4. Validation of the MODIS LAI Product in Coniferous Forests of Ruokolahti, Finland

5. Concluding Remarks

6. Future Directions

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Other WorksDowty, D., Frost, P., Lesolle, P., Midgley, G., Mukelabai, M., Otter, L., Privette, J., Ringrose, S., Scholes, B., Wang, Y., (2000), Summary of the SAFARI 2000 wet season field campaign along the Kalahari transect. The Earth Observer. 12:29-34.Shabanov, N. V., Wang, Y., Buermann, W., Dong, J., Hoffman, S., Tian, Y. Knyazikhin, Y Gower, S. T. and Myneni, R. B., (2001), Validation of the radiative transfer principles of the MODIS LAI/FPAR algorithm with data from the Harvard forest, Remote Sens. Environ. (in review). Myneni, R. B., Hoffman, S., Knyazikhin, Y. , Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R. and Running, S. W., (2001), Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data, Remote Sens. Environ. (in press). Tian, Y., Wang, Y., Zhang, Y., Knyazikhin, Y., Bogaert, J., and Myneni, R. B., (2001), Radiative Transfer Based Scaling of LAI/FPAR Retrievals From Reflectance Data of Different Resolutions, Remote Sens. Environ. (in press).

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Other Works (cont.)

Privette, J.L., Myneni, R. B., Knyazikhin, Y., Mukufute, M., Robert, G., Tian, Y., Wang, Y. and Leblanc, S.G., (2001), Early Spatial and Temporal Validation of MODIS LAI Product in Africa, (in press).Buermann, W, Wang, Y., Dong, J., Zhou, L., Zeng, X., Dickinson, R. E., Potter, C. S. and Myneni, R. B., (2001), Analysis of a multi-year global vegetation leaf area index data set, J. Geophys. Res. (in press).Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou, L., Buermann, W., Dong, J., Veikkanen, B., Hame, T., Ozdogan M., Knyazikhin Y., and Myneni, R. B., (2001), Multiscale Analysis and Validation of the MODIS LAI Product over Maun, Botswana, I. Uncertainty assessment. Remote Sens. Environ. (Accepted Jan. 2002). Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou, L., Buermann, W., Dong, J., Veikkanen, B., Hame, T., Ozdogan M., Knyazikhin Y., and Myneni, R. B., (2001), Multiscale Analysis and Validation of the MODIS LAI Product over Maun, Botswana, II. Sampling strategy. Remote Sens. Environ. (Accepted Jan. 2002).

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Motivation

LAI and FPAR are two key variables for climate and most model studies. They are operationally derived from measurements of the MODIS instrument aboard TERRA .

How do uncertainties in input and model influence the performance of the MODIS LAI/FPAR algorithm?

Is the parameterization of the law of energy conservation valid in the design of the algorithm?

What is the uncertainty of the MODIS LAI product?

5 of 51

Wang et. al. (2001), Investigation of product accuracy as a function of input and model uncertainties: Case study with SeaWiFS and MODIS LAI/FPAR algorithm, Remote Sens. Environ., 78:296-311.

Summary of Presentation 1. Motivation

2. Investigation of Retrieval Quality as a Function of Input and Model Uncertainty





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Data

Atmospherically corrected and monthly composited multispectral Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) surface reflectance data.

Spatial resolution: 8-km.

Spectral bands: Blue (443 nm), green (555 nm), red (670 nm) and NIR (865 nm).

Global biome classification map derived from AVHRR Pathfinder data (Myneni et. al., 1997).

6 biome types: Grasses and cereal crops, shrubs, broadleaf crops, savannas, broadleaf forests and needle forests.

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Two Types of Uncertainties

Model Uncertainty• Determined by the range

of natural variation in biophysical parameters not accounted by the model.

Uncertainty in the land surface reflectance• Determined by the in-orbit data errors and data processing

to correct for atmospheric and other environmental effects.

France Campaign

0.00

0.04

0.08

0.12

0.16

0.20

400 500 600 700 800 900 1000

Wavelength, nm

Coe

ffici

ent o

f var

iatio

n of

leaf

alb

edo

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Inverse Problem

Ideal Condition: if uncertainties are known, true LAI can be solved accurately.

In the algorithm, the uncertainty information is not available. Therefore, the above inequality is solved.

Model-Observation

0%

10%

20%

30%

40%

50%

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|| Model(LAItrue)-Observation ||

|| Model(LAItrue)-Observation || =

MODIS LAI/FPAR Algorithm Formulation

The algorithm retrieves distribution functions of all possible solutions that satisfy the above inequality. The mean values and their dispersions are taken as final solution.

0

1

2

3

0 2 4 6 8 10Leaf area index

Pro

bab

ility

den

sity

LAI=0.1

LAI=2

LAI=3LAI=5

LAI=1

1),(),,(1

2

1

00

N

k k

vkvk dpr

N

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Input Output

Retrieval Quality1. Dispersion:

The root mean square deviation of the solution distribution function. It indicates the reliability of the retrieved LAI/FPAR fields.

2. Retrieval Index (RI):

pixels vegetatedofnumber total

pixels retrieved ofnumber RI

3. Saturation Index (SI):

valuesLAI retrieved ofnumber total

saturation of conditionsunder retrieved LAIs ofnumber SI

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Band Independent Uncertainty, Dispersion

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Band Independent Uncertainty, Retrieval Index

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Band Independent Uncertainty, Saturation Index

Spectral Bands Used Biome Type Red NIR Blue Green Grasses/Ce-

real crops, %

Shrubs, % Broadleaf Crops, %

Savannas, %

Broadleaf Forests, %

Needle Forests, %

13.0 5.8 16.9 10.3 62.2 49.5 13.0 3.7 11.3 10.6 60.3 44.7 12.2 3.9 10.6 11.4 60.5 43.3

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Band Dependent Uncertainty and Overall Uncertainty

Spectral Band 1 (Red) 2 (NIR) 3 (Blue) 4 (Green) Center of Band, nm 670 865 443 555 Bandwidth, nm 20 40 20 20 Relative Error, % 10-33 3-6 50-80 5-12

k, dimensionless 0.2 0.05 0.8 0.1

Theoretical estimation of relative uncertainties in atmospherically corrected

surface reflectances (Vermote, 2000)

NNN 21)(

An overall uncertainty is defined as:

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Band Dependent Uncertainty, Dispersion

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Band Dependent Uncertainty, Retrieval Index

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Band Dependent Uncertainty, Saturation Index

Spectral Bands Used Biome Type Red NIR Blue Green Grasses/Cereal

Crops, % Shrubs,

% Broadleaf Crops, %

Savannas, %

Broadleaf Forests, %

Needle Forests, %

8.6 1.4 15.1 8.4 48.8 21.4 6.5 0.2 6.2 8.9 44.1 9.55

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Test of Physics

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SeaWiFS Global LAI in January, April, July and October

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Conclusions

Uncertainties in land surface reflectances and models used in the algorithm determine the quality of retrieved LAI and FPAR fields.

Accurate information about uncertainty in surface reflectance and model can improve the retrieval quality.

The more the measured information and the more accurate this information, the more reliable and accurate is the algorithm output.

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Wang et. al. (2002), Hyperspectral remote sensing of vegetation canopy leaf area index and foliage optical properties, Remote Sens. Environ., (submitted).







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Radiative Transfer Equation Decomposition

Black Soil Problem

rbs+tbs+abs=1

S Problem

rs+ts+as=1

23 of 51*credit: Chandrasekhar, 1950.

Canopy Structure Parameters

r()

()

t()

Wavelength, nm

Wavelength, nm

Wavelength, nm

)(t)()(t)(

)(t)(t),(p

1100

1010t

)(i)()(i)(

)(i)(i),(p

1100

1010i

i() : interception

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0

5

10

15

20

25

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

0

5

10

15

20

25

0 0.2 0.4 0.6 0.8 1 1.2 1.4

f(p)

=dF

(p)/

dp

pi

f(p)

=dF

(p)/

dp

pt

Spectral Invariance of pt and pi

const)(t)()(t)(

)(t)(t

1100

10

const)(i)()(i)(

)(i)(i

1100

10

PtPi

*credit: Panferov et. al. 2001

p_values depend on canopy structure and illumination geometry

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Uncollided and Collided Radiation

t

qtp t

t

i

qip t

i

)1(

Canopy transmittance is the sum of collided and uncollided radiation arriving at the canopy bottom.

Uncollided radiation qt is radiation arriving at the canopy bottom without experiencing any collisions. It equals canopy transmittance t () when single scattering albedo is zero.

Collided radiation is the radiation which experienced at least one collision (t() – qt).

()pt is the collided portion of canopy transmittance.

()pi is the multi-collided portion of canopy interception.

26 of 51*Credit: Shabanov et. Al, 2002.

Information Content of Hyperspectral Data

3,2,1,

1)()()(

)(1

1

)(1

)()(1

)(

k

atr

p

qap

qt

kkk

ik

t

k

k

tk

tk

Known variables Unknown variables

t(), r() (), pi, pt,, qt, a()

*r(), () t(), pi, pt, qt, a()

r(), qt, pi, pt (), t(), a() (), pi, pt, qt r(), t(), a()

27 of 51* Parameters used in MODIS LAI/FPAR algorithm

Data

The hyperspectral canopy transmittance and reflectance data measured in a 100x150m needle leaf forest plot in Ruokolahti, Finland will be used.

400 500 600 700 800 9000.0

0.2

0.4

0.6

0.8

Tra

nsm

itta

nce

Wavelength (nm)

400 500 600 700 800 9000.0

0.1

0.2

0.3

0.4

HD

RF

Wavelength (nm)

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Retrieval of Uncollided Radiation qt

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

400-487nmF

req

uen

cy

qt

0.0 0.2 0.4 0.6 0.8 1.00.0

0.1

0.2

0.3

0.4

0.5

0.6

487-555nm

Fre

qu

ency

qt

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

555-650nm

Fre

qu

ency

qt

0.0 0.2 0.4 0.6 0.8 1.00.0

0.1

0.2

0.3

0.4

0.5

650-900nm

Fre

qu

ency

qt

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Biophysical Parameters related to qt

Fraction of beam radiation (fdir).

0.0 0.2 0.4 0.6 0.8 1.00.0

0.1

0.2

0.3

0.4

80o

60o

43o

20o

0o

Po

rtio

n o

f U

nco

llid

ed R

adia

tio

n

Fraction of Direct Light, fdir

Leaf area index.

Retrieved LAI = 2.02

Measured LAI =1.95

Ground cover.

)(

)(||

0dirt,

0

n0

0

0n

0

n0

)(1

))(

)(||

||

)(LAIexp(1))(LAIexp(1

G

G

n

q

G

GGGq

)||

)(LAIexp()(

0

00dirt,

Gq

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Retrieval of Single Scattering Albedo

20000--100000 16000--20000 12000--16000 8000 --12000 4000 --8000 0 --4000

Wavelength(nm)

Sin

gle

Sca

tter

ing

Alb

edo

400 500 600 700 800 9000.0

0.2

0.4

0.6

0.8

1.0

400 500 600 700 800 9000.0

0.2

0.4

0.6

0.8

Mean Mean +/- Stdev

Sin

gle

Sca

tter

ing

Alb

edo

Wavelength (nm)

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Bivariate distribution function of solution of single scattering albedo

Regression curve of the bivariate distribution function

Retrieval of pt Value

0.0 0.2 0.4 0.6 0.8 1.00.00

0.05

0.10

0.15

0.20

0.25

487-555nm

Fre

qu

ency

pt,BS

0.0 0.2 0.4 0.6 0.8 1.00.0

0.1

0.2

0.3

0.4

555-650nm

Fre

qu

ency

pt,BS

0.0 0.2 0.4 0.6 0.8 1.00.00

0.05

0.10

0.15

0.20

0.25

0.30

650-900nm

Fre

qu

ency

pt,BS

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Influence of Soil Reflectance

0.3 0.4 0.5 0.6 0.7 0.80.3

0.4

0.5

0.6

0.7

0.8

Cal

cula

ted

Ab

sorp

tan

ce

Measured Absorptance

)(a))(r)(t()(r1

)(a)(t)(r1)(a BSSSS

BS

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Conclusions

A small set of independent variables seem to suffice to describe their spectral response to incident solar radiation. The spectra of soil reflectance and single scattering albedo, canopy

transmittance and absorptance normalized by single scattering albedo, the portion of uncollided and collided canopy transmittance and normalized interception.

These variables satisfy a simple system of equations and constitute a set that fully describes the law of energy conservation in vegetation canopy at any wavelength of the solar spectrum.The equation system is a closed system, which means once information on some of the variables is available, the rest can be retrieved through this system.

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Wang et. al. (2002), Validation of the MODIS LAI Product in Coniferous Forest of Ruokolahti, Finland, Remote Sens. Environ., (in preparation).







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Strategy

Field Measurements

Fine resolution satellite image

Fine resolution LAI map

Compare with MODIS LAI product

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Data

Field measured LAI dataRuokolahti, Finland needle leaf forest site

Air-borne and Satellite image

2 m resolution air-borne CCD imageETM+ dataMODIS LAI product

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Ruokolahti Field Campaign

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Ruokolahti Campaign Sampling Strategy

1000m

1000m

25*25m grid

100*150m

25*25m grid

150*100m

25*25m grid

200*200m

50X50m

Young Dense

Regular

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Pixel-by-pixel vs. Patch-by patch comparison

Pixel-by-pixel comparison

High geolocation error

Non-representative sampling

Patch-by-patch comparison

Reduced geolocation error

Small amount of sampling is sufficient to characterize mean

40 of 51Credit: Tian et. Al., 2002

Image Segmentation

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ETM+ image over campaign site Segmentation result

Correlation between Simple Ratio (SR) and Field-measured LAI

SR = 0.8958LAI + 5.852

R2 = 0.19780

2

4

6

8

10

12

14

0 1 2 3 4 5

LAI

SR

SR = 1.5186LAI + 4.6783

R2 = 0.81512

4

6

8

10

0 0.5 1 1.5 2 2.5 3

LAI

SR

Pixel scale Patch scale

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Patch Level Correlation between Field-measured LAI and Reduced Simple Ratio

(RSR)

RSR = 1.9349LAI + 1.6652

R2 = 0.9080

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5 3

LAI

RSR

RSR includes Shortwave Infrared (SWIR) band.

RSR can suppress background influence and the difference between land cover types. (Brown et. al., 2000).

There is better correlation between field-measured LAI and RSR.

])min()max(

)min(1[

SWIRSWIR

SWIRSWIR

red

NIRRSR

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Fine Resolution LAI Map

10 km area 1 km campaign site

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MODIS LAI and FPAR Algorithm at 30 m Resolution

At 30 m resolution, the algorithm retrievals are greater than field measurements in this site. The difference between them is a decreasing function of LAI.

The algorithm assumes no biome mixtures within the 30m resolution pixel. However, this assumption is violated at this site because the mixture of understory vegetation and needle forests.

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y = 0.7039x + 0.9073

R2 = 0.8415

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

Measured LAI

Retri

eved

LAI

MODIS QA

MODIS QA map in the 10x10 km area (day 177, 2000). Green: LAI value produced by the main algorithm; Red: LAI is produced by the backup algorithm; Blue: cloud contaminated pixel; Black: water or barren.

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Validation of MODIS LAI Product

1.8 -- 2.0 1.6 -- 1.8 1.4 -- 1.6 1.2 -- 1.4 1.0 -- 1.2 0.80 -- 1.0 0.60 -- 0.80 0.40 -- 0.60 0.20 -- 0.40 0 -- 0.20

y = 1.0124xR2 = 0.8508

y = 1.0594x - 0.0607R2 = 0.8526

0

0.4

0.8

1.2

1.6

2

0 0.4 0.8 1.2 1.6 2

ETM LAI

MO

DIS

LA

IContour plot of LAI aggregated from the fine resolution ETM+ LAI map

Patch scale correlation between the MODIS and aggregated LAI map

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Conclusions

Patch scale comparison is more reliable than pixel scale comparison.

Improved correlation between field measurements and the reduced simple ratio suggests that shortwave infrared band may provide valuable information for needle leaf forests.

MODIS LAI algorithm can only works well for relatively pure pixels at 30 m resolution for needleleaf forests, improvements are needed.

Comparison of MODIS LAI product with aggregated fine resolution LAI map indicates satisfactory performance of the algorithm at coarse resolution.

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Concluding Remarks

Uncertainties in input spectral bands and models are critical for the retrieval of biophysical parameters of highest possible quality. Their use can increase the number of high quality retrievals .Assessment of the parameterization of the algorithm in light of the law of energy conservation indicates that spectra of soil and single scattering albedo combined with canopy interception, transmittance and their collided portions at a fixed reference wavelength are sufficient to simulate the spectral response of a vegetation canopy to incident solar radiation. They satisfy a closed equation system.Investigation of the relationship between field data on LAI and 30m ETM+ images indicates that comparisons at the patch level are more reliable than the pixel level. Comparisons indicate the need for improvements in the algorithm for needleleaf forests at fine resolution. The MODIS LAI product agrees with ETM+ derived LAI at coarse resolution in Ruokolahti needle forests site.

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Future Directions

It is possible to include soil reflectance in the system of equations I derived. This may result in more accurate solutions and also the possibility of retrieving spectral soil reflectance using hyperspectral data.

Shortwave infrared data should perhaps be included in LAI and FPAR retrievals over boreal forests, as there is now considerable evidence to this effect.

More field LAI data should be collected at different locations and periods, representative of the major vegetation types and their phenology, to comprehensively validate both the algorithm and the products.

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The end.

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Fraction of Forest over 10 km area

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 0.2 0.4 0.6 0.8 1

Fraction of Forest (%)

Fre

qu

ency

More than 70% of pixels are forest

dominant

Yujie Wang Geography Department, Boston University

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