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
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
6 of 51
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
8 of 51
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
10 of 51
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.
21 of 51
Wang et. al. (2002), Hyperspectral remote sensing of vegetation canopy leaf area index and foliage optical properties, Remote Sens. Environ., (submitted).
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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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
25 of 51
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
30 of 51
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)
31 of 51
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
33 of 51
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.
34 of 51
Wang et. al. (2002), Validation of the MODIS LAI Product in Coniferous Forest of Ruokolahti, Finland, Remote Sens. Environ., (in preparation).
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
35 of 51
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
43 of 51
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
47 of 51
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
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