Transcript Assessment of the MODIS LAI and FPAR Algorithm: Retrieval

Ph. D. dissertation Defense
Assessment of the MODIS LAI and FPAR Algorithm:
Retrieval Quality, Theoretical Basis and Validation
Yujie Wang
Geography Department, Boston University
Dissertation committee
Ranga B. Myneni
Yuri Knyazikhin
Mark A. Friedl
Curtis E. Woodcock
Jeffrey L. Privette
1 of 51
Summary of Presentation
1.
2.
3.
4.
5.
6.
Motivation
Investigation of Retrieval Quality as a function of Input
and Model Uncertainty
Parameterization of the Algorithm in Light of the Law of
Energy Conservation
Validation of the MODIS LAI Product in Coniferous
Forests of Ruokolahti, Finland
Concluding Remarks
Future Directions
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Other Works
Dowty, 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?
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Summary of Presentation
1.
2.
3.
4.
5.
6.
Motivation
Investigation of Retrieval Quality as a Function of
Input and Model Uncertainty
Parameterization of the Algorithm in Light of the Law of
Energy Conservation
Validation of the MODIS LAI Product in Coniferous
Forests of Ruokolahti, Finland
Concluding Remarks
Future Directions
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.
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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
• Determined by the range
of natural variation in
biophysical
parameters
not accounted by the
model.
0.20
Coefficient of variation
of leaf albedo
 Model Uncertainty
France Campaign
0.16
0.12
0.08
0.04
0.00
400
500
600
700
800
900
1000
Wavelength, nm
 Uncertainty in the land surface reflectance
• Determined by the in-orbit data errors and data processing
to correct for atmospheric and other environmental effects.
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Inverse Problem
|| Model(LAItrue)-Observation || = 
Ideal Condition: if uncertainties  are known, true LAI
can be solved accurately.
|| Model(LAItrue)-Observation ||  
50%
40%
30%
20%
10%
0. <0.
01
0
<e 1
<0
0.
.0
05
<e 5
<0
0.
.1
10
<e 0
<0
0.
.
15 15
<e
0.
<0
20
.
<e 2
<0
0.
.2
25
<e 5
<0
0.
.3
30
<e 0
<0
.3
5
0.
35
<e
0%
0<
e
In the algorithm, the
uncertainty information
is
not
available.
Therefore, the above
inequality is solved.
Model-Observation
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MODIS LAI/FPAR Algorithm Formulation
2
 rk ( v ,  0 , p )  d k ( v ,  0 ) 
1

  1

N k 1 
k

N
Input
Output
Probability density
3
LAI=0.1
LAI=1
2
LAI=2
1
LAI=3
LAI=5
0
0
2
4
6
Leaf area index
8
10
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.
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Retrieval Quality
1. 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):
RI 
number of retrievedpixels
totalnumber of vegetatedpixels
3. Saturation Index (SI):
SI 
number of LAIs retrieved under conditions of saturation
total number of retrieved LAI values
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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
Red NIR Blue Green









Grasses/Cereal crops, %
13.0
13.0
12.2
Shrubs, %
5.8
3.7
3.9
Biome Type
Broadleaf
Crops, %
Savannas, %
16.9
10.3
11.3
10.6
10.6
11.4
Broadleaf
Forests, %
62.2
60.3
60.5
Needle
Forests, %
49.5
44.7
43.3
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Band Dependent Uncertainty and Overall
Uncertainty
Theoretical estimation of relative uncertainties in atmospherically corrected
surface reflectances (Vermote, 2000)
Spectral Band
Center of Band, nm
Bandwidth, nm
Relative Error, %
k, dimensionless
1 (Red)
670
20
10-33
0.2
2 (NIR)
865
40
3-6
0.05
3 (Blue)
443
20
50-80
0.8
4 (Green)
555
20
5-12
0.1
An overall uncertainty is defined as:
 ( N )  N 1 2  N
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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
Red NIR Blue Green Grasses/Cereal
Crops, %
8.6


6.5



Shrubs,
%
1.4
0.2
Biome Type
Broadleaf Savannas,
Crops, %
%
15.1
8.4
6.2
8.9
Broadleaf
Needle
Forests, % Forests, %
48.8
21.4
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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Summary of Presentation
1.
2.
3.
4.
5.
6.
Motivation
Investigation of Retrieval Quality as a function of Input
and Model Uncertainty
Parameterization of the Algorithm in Light of the Law of
Energy Conservation
Validation of the MODIS LAI Product in Coniferous
Forests of Ruokolahti, Finland
Concluding Remarks
Future Directions
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
rbs+tbs+abs=1
Black Soil Problem
S Problem
rs+ts+as=1
*credit: Chandrasekhar, 1950.
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Canopy Structure Parameters
r()
Wavelength, nm
()
Wavelength, nm
t()
Wavelength, nm
p t ( 0 ,  1 ) 
t( 0 )  t( 1 )
( 0 )t( 0 )  ( 1 )t( 1 )
p i ( 0 ,  1 ) 
i(  0 )  i(  1 )
( 0 )i( 0 )  ( 1 )i( 1 )
i() : interception
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Spectral Invariance of pt and pi
25
Pt
20
f(p)=dF(p)/dp
f(p)=dF(p)/dp
25
15
10
5
0
15
10
5
0
0
0.2 0.4 0.6 0.8
pt
1
1.2 1.4 1.6 1.8
2
Pi
20
0
0.2
0.4
0.6
0.8
1
1.2
1.4
pi
i(  0 )  i(  1 )
t( 0 )  t( 1 )
 const
 const
( 0 )i( 0 )  ( 1 )i( 1 )
( 0 )t( 0 )  ( 1 )t( 1 )
p_values depend on canopy structure and illumination geometry
*credit: Panferov et. al. 2001
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Uncollided and Collided Radiation
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 
t    qt
t  
i   (1  qt )
   pi 
i 
*Credit: Shabanov et. Al, 2002.
()pt is the collided portion of
transmittance.
canopy
()pi is the multi-collided portion of canopy
interception.
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Information Content of Hyperspectral Data
qt

 t ( k )  1  ( ) p
k
t

1  qt
 a ( k ) 

 1  ( k ) 1  ( k ) pi ,


r (  k )  t (  k )  a (  k )  1


k  1, 2, 3
Known variables
Unknown variables
t(), r()
*r(), ()
(), pi, pt,, qt, a()
t(), pi, pt, qt, a()
(), t(), a()
r(), t(), a()
r(), qt, pi, pt
(), pi, pt, qt
* Parameters used in MODIS LAI/FPAR algorithm
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Data
The hyperspectral canopy transmittance and reflectance
data measured in a 100x150m needle leaf forest plot in
Ruokolahti, Finland will be used.
0.4
0.8
0.3
HDRF
Transmittance
0.6
0.4
0.1
0.2
0.0
400
0.2
500
600
700
Wavelength (nm)
800
900
0.0
400
500
600
700
800
900
Wavelength (nm)
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Retrieval of Uncollided Radiation qt
0.8
0.6
400-487nm
487-555nm
0.5
Frequency
Frequency
0.6
0.4
0.2
0.4
0.3
0.2
0.1
0.0
0.0
0.2
0.4
0.6
0.8
0.0
0.0
1.0
0.2
0.4
qt
0.6
0.8
1.0
qt
0.5
0.8
650-900nm
555-650nm
0.4
Frequency
Frequency
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
qt
0.8
1.0
0.3
0.2
0.1
0.0
0.0
0.2
0.4
0.6
0.8
1.0
qt
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Biophysical Parameters related to qt
Fraction of beam
radiation (fdir).
Leaf area index.
qt,dir ( 0 )  exp(LAI
G ( 0 )
)
| 0 |
Retrieved LAI = 2.02
Measured LAI =1.95
Portion of Uncollided Radiation
0.4
0
0.3
o
20
o
43
0.2
o
60
0.1
o
80
0.0
0.0
0.2
1  q n  exp(LAI  G ( n ))  1  exp(LAI
 1  q t,dir ( 0 )
0.4
0.6
0.8
1.0
Fraction of Direct Light, fdir
Ground cover.
| 0 |
o
G ( 0 ) |  0 | G ( n )

)
| 0 |
G ( 0 )
G (n )
G (0 )
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Retrieval of Single Scattering Albedo
0.8
20000--100000
16000--20000
12000--16000
8000 --12000
4000 --8000
0 --4000
0.8
Single Scattering Albedo
Single Scattering Albedo
1.0
0.6
0.4
0.2
Mean
Mean +/- Stdev
0.6
0.4
0.2
0.0
400
0.0
400
500
600
700
800
900
Wavelength(nm)
Bivariate distribution function of
solution of single scattering albedo
500
600
700
800
900
Wavelength (nm)
Regression curve of the bivariate
distribution function
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Retrieval of pt Value
0.25
0.4
487-555nm
555-650nm
0.3
Frequency
Frequency
0.20
0.15
0.10
0.05
0.00
0.0
0.2
0.4
0.6
0.8
1.0
pt,BS
0.2
0.1
0.0
0.0
0.2
0.4
0.6
0.8
1.0
pt,BS
0.30
650-900nm
Frequency
0.25
0.20
0.15
0.10
0.05
0.00
0.0
0.2
0.4
0.6
0.8
1.0
pt,BS
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Influence of Soil Reflectance
Calculated Absorptance
0.8
0.7
0.6
0.5
0.4
0.3
0.3
0.4
0.5
0.6
0.7
0.8
Measured Absorptance
a ()  1  r ()  t ()  a BS () 

(t S ()  rS ())  a BS ()
1    rS ()
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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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Summary of Presentation
1.
2.
3.
4.
5.
6.
Motivation
Investigation of Retrieval Quality as a function of Input
and Model Uncertainty
Parameterization of the Algorithm in Light of the Law of
Energy Conservation
Validation of the MODIS LAI Product in Coniferous
Forests of Ruokolahti, Finland
Concluding Remarks
Future Directions
Wang et. al. (2002), Validation of the MODIS LAI Product in Coniferous
Forest of Ruokolahti, Finland, Remote Sens. Environ., (in preparation).
35 of 51
Strategy
Field
Measurements
Fine
resolution
LAI map
Fine resolution
satellite image
Compare
with
MODIS LAI
product
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Data
Field measured LAI data
Ruokolahti, Finland needle leaf forest
site
Air-borne and Satellite
image
2 m resolution air-borne CCD
image
ETM+ data
MODIS LAI product
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Ruokolahti Field Campaign
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Ruokolahti Campaign Sampling Strategy
50X50m
25*25m
grid
25*25m grid
100*150m
150*100m
Dense
Young
1000m
Regular
25*25m grid
200*200m
1000m
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Pixel-by-pixel vs. Patch-by patch comparison
Pixel-by-pixel comparison
Patch-by-patch comparison
High geolocation error
Reduced geolocation error
Non-representative sampling
Small amount of sampling is
sufficient to characterize mean
Credit: Tian et. Al., 2002
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Image Segmentation
ETM+ image over campaign site
Segmentation result
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Correlation between Simple Ratio (SR) and
Field-measured LAI
14
10
12
8
8
SR
SR
10
6
6
4
4
SR = 0.8958LAI + 5.852
R2 = 0.1978
2
SR = 1.5186LAI + 4.6783
R2 = 0.8151
0
2
0
1
2
3
LAI
Pixel scale
4
5
0
0.5
1
1.5
2
2.5
3
LAI
Patch scale
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Patch Level Correlation between Fieldmeasured LAI and Reduced Simple Ratio
(RSR)
RSR 
8
7
RSR includes Shortwave
Infrared (SWIR) band.
6
RSR
 NIR
 SWIR  min( SWIR )
[1 
]
 red
max( SWIR )  min( SWIR )
5
RSR can suppress background
influence and the difference
between land cover types.
(Brown et. al., 2000).
4
3
2
RSR = 1.9349LAI + 1.6652
R2 = 0.908
1
0
0
0.5
1
1.5
LAI
2
2.5
3
There is better correlation
between field-measured LAI
and RSR.
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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
3
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.
Retrieved LAI
2.5
2
y = 0.7039x + 0.9073
R2 = 0.8415
1.5
1
0.5
0
0
0.5
1
1.5
Measured LAI
2
2.5
3
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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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
2
1.6
MODIS LAI
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 = 1.0594x - 0.0607
R2 = 0.8526
1.2
y = 1.0124x
R2 = 0.8508
0.8
0.4
0
0
0.4
0.8
1.2
1.6
2
ETM LAI
Contour 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.
50 of 51
The end.
51 of 51
Fraction of Forest over 10 km area
0.4
0.35
Frequency
0.3
0.25
0.2
0.15
0.1
0.05
0
0
0.2
0.4
0.6
Fraction of Forest (%)
0.8
1
More than 70% of pixels are forest
dominant