advanced optical theory - 2 retrieval of...
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Advanced Optical Theory - 2Retrieval of Information
Jose Moreno
3 September 2007, Lecture D1La5
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 2
ADVANCED OPTICAL THEORYPart II: Retrieval of information
Information content of optical data in spectral and angular domains
Forward modelling of surface reflectance: soil, leaf and canopy models
Information retrieval based on optimised spectral indices
Information retrieval based on model inversion techniques
Advanced retrieval techniques: multi-step procedures
Validation of retrievals
Scaling issues
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 3
Information content ofoptical data in the spectal
and angular domains
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 4
MERIS Landsat TM
HyMapCHRIS
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250023002100190017001500130011009007005003003000
2 0
4 0
6 0
8 0
1 0 0
1 2 0
1 4 0
1 6 0
wavelength (nm)
WATER ABSORPTION
spec
ific
abso
rpti
on c
oeff
icie
nt (
cm
)-1
CHLOROPHYLL ABSORPTION
250023002100190017001500130011009007005003003000 . 0 0
0 . 0 1
0 . 0 2
0 . 0 3
0 . 0 4
0 . 0 5
0 . 0 6
0 . 0 7
0 . 0 8
wavelength (nm)
spec
ific
abso
rpti
on c
oeff
icie
nt (
cm
g
)2
-1μ
LAI fCoverTH
E K
EY IN
FOR
MA
TIO
N C
ON
TEN
T
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 6
l iqu id w at er abs orp tion b ands
hot-s pot eff ect
v iew angle (principal p lane)
w av elength
refle
ctan
ce
chlorop hy ll abs orp tion
Sun z enith angle
Sensor scan
plane
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0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.60
5
10
15
20
25
30
35
40
45
50
55
60
65
wavelength ( m)
refle
ctan
cegreen vegetation (alfalfa)senescent vegetation (barley)
μ
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DAISEX-1999Barrax / HyMap data /
φ R
Hot-Spot Target
ϑs
h
Sun
Sensor
B
B
A
A
Hot-Spot Target
Optimised observation geometry: - flight time and flight direction - maximum solar elevation angle High radiometric sensitivity
SouthNorth
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- several view angles at maximum solar elevation- several flights af different solar elevations- maximisation of angular range (hot-spot and dark-spot)
Planning experiments to test angular information
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0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
0
5
10
15
20
25
30
35
40
45
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
SUGAR BEET
wavelength (nm)
wavelength (nm)
refle
ctan
cere
flect
ance
ALFALFA
Line 1 , noonLine 2 , noonLine 1 , morningLine 2 , morningLine 1 , af t ernoonLine 2 , af te rnoon
SV6 - C03 UTM-X: 577783 UTM-Y: 4324794 LAI = 1.71 fCover = 0.61
Line 1 , noonLine 2 , noonLine 1 , morningLine 2 , morningLine 1 , af t ernoonLine 2 , af te rnoon
V16 - C01 UTM-X: 577550 UTM-Y: 4324069 LAI = 1.84 fCover = 0.95 Hyperspectral versus
directional information
A rather Lambertian surface
A highly anysotropic surface
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POLDER dataDAISEX-99 / Barrax, Spain
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95
wavelength (μm)
POLDER spectral channels
multiangular coverage
Four flights, different solar elevations: - early morning - mid-morning - noon - afternoon Simultaneous with HYMAP and DAIS data
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specular
POLDER data
Irrigated alfalfa field
Channel 3 (550 nm)
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POLDERnoon flight
POLDERmorning flight
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3º - 10º
6º - 20º
20º - 45º
Angular effects are especially critical for airborne data
Daedalus campaignEvora, Portugal
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13 m/pixel (nominal) – image: 1 m/pixel
--33ºº --11ºº 11ºº 33ºº 55ºº 88ºº
3.6 m/pixel (nominal) – image: 1 m/pixel
--55ºº 33ºº 1010ºº 1818ºº 2424ºº 3232ºº
50m
1.5 m/pixel (nominal) – image: 1 m/pixel
--3838ºº --2323ºº --88ºº 1010ºº 2424ºº 4040ºº
Daedalus campaignEvora, Portugal
Angular effects versusspatial resolution
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MISRTerra
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15km
Beginimaging
Endimaging
Line-of-sightof imager
Image2Image4Image5 Image1Image3
Scanning speed = 1/5 of spacecraft ground speed
CHRIS / PROBA
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62 spectral bands34 m resolution5 view angles
CHRIS/PROBA DATA
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Forward modelling of surface reflectance:
soil, leaf and canopy models
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FORWARD MODELLINGLeaf Canopy Environment
Soil / LitterWater /Snowbackground
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BARE SOILREFLECTANCE
only top soilis relevant
soil
prof
ile
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Hapke (1981) bidirectional reflectance model
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MODEL
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ACTUAL DATA (HYMAP)
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shadow-hiding mechanismreflections and refractions throughsurfaces and insidethe particle volume(coherent scattering)
opposition effect
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LEAF MODEL
- Must account for reflectance andtransmitance of the leaves
- Explicit description of spectral variations
- Explicit description of angular effectsin both reflectance and transmittance
- Accountig for fluorescence emission
SEVERAL MODELS AVAILABLE, BUT NONE IS DESCRIBING ALL THE EFFECTSIN A PROPER WAY (USER MUSTCHOOSE WHICH EFFECTS ARE MORERELEVANT FOR A GIVEN APPLICATION)
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Plate models
Allen et al. (1969, 1970), Gausman et al. (1970), Jacquemoud et al. (1990, 1996, PROSPECT), Fourty et al. (1996), Baret and Fourty (1997)
ρ
τN
layers
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Discrete objects models
Dawson et al. (1997, LIBERTY), Ganapol et al. (1998, LEAFMOD), Ma et al. (1990)
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N-flux models
Allen and Richardson (1968), Andrieu et al. (1988), Fukshansky et al. (1991), Martinez v. Remisowsky et al. (1992), Richter and Fukshansky (1996), Yamada and Fujimura (1991), Conel et al. (1993)
Ic
Id
Jc
Jd
ρ
τs = scattering coefficientk = absorption coefficient
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Stochastic models
upperepidermis
palisademesophyll
spongymesophyll
lowerepidermis
upperepidermis
palisademesophyll
spongymesophyll
lowerepidermis
absorbed
absorbed
absorbed
absorbed
scattered
scattered
scattered
scattered
illuminationtop
directlyreflected
diffusereflected
illuminationbottom
directlyreflected
diffusereflected
Tucker and Garratt (1977, LFMOD1), Lüdeker and Günther (1990), Maier et al. (1997, SLOP)
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Ray tracing models
Govaerts et al. (1996, RAYTRAN), Jacquemoud et al. (1997), Baranoskiand Rokne (1997, 1998, 2000, ABM), Ustin et al. (2001)
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1 0-2
1 0-1
1 00
1 01
1 02
1 03
1 04
1 05
1 07
1 06
λ (μm)
1 00
1 01
1 0- 1
Rea
l (n)
75342
1 TM 6TM
PLC
WATER SCATTERING
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1 0-2
1 0-1
1 00
1 01
1 02
1 03
1 04
1 05
1 07
1 06
1 01
1 00
1 0-1
1 0-2
1 0- 3
1 0-4
1 0-5
1 0-6
1 0-7
1 0-8
Imag
(n)
λ (μm)
TM 6
75
1
WATER ABSORPTION
PL
C
234
TM
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0 2 0 4 0 6 0 8 0 1 0 0 1 2 00
2 0
4 0
6 0
8 0
1 0 0
1 2 0
Pure Water
Bou
nded
Wat
er
Absorpt ion coefficient of liquid water (1/cm)
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0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.0000
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.0010
0.0011
0.0012
0.0013
0.0014
400 500 600 700 800 900 1000
spec
ific
abso
rptio
n co
effic
ient
(PR
OS
PE
CT)
wavelength (nm)
spec
ific
abso
rptio
n co
effic
ient
(LIB
ER
TY)
PROSPECT-I
PROSPECT-II
LIBERTY
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CHLOROPHYLL ABSORPTION
0.00
0.01
0.02
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0.06
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0.10
OldNew
400 500 600 700 800 900 1000
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 46
05
1015202530354045505560657075808590
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
spec
ific
abso
rptio
n co
effic
ient
(cm
g
)2
-1
wavelength (nm)
CELLULOSE+LIGNIN ABSORPTION
PROTEIN ABSORPTION
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0
10
20
30
40
50
60
70
80
90
100
110
OldNew
500 1000 1500 2000 2500
DRY MATTER ABSORPTION
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PROSPECT - Sensitivity analysis
400 600 800 1000 1200 1400 1600 1800 2000 2200 24000
10
20
30
40
50
60
70
80
90
100
Wavelength (nm)
Con
tribu
tion
(%)
NCabCwCm
After Jacquemoud et al.
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 50
-60
-40
-20
0
20
40
60
80
100
120
140
400 450 500 550 600 650
zeaxanthin-violaxanthin
abso
rptio
n di
ffere
nce
wavelength (nm)
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
400 450 500 550 600 650
violaxanthinzeaxanthin
wavelength (nm)
abso
rptio
n co
effic
ient
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 51
Sodankyla, Finland
SIFLEX data
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 52
absorption
An absorption spectrum (a) shows a vibrational structure characteristic of the upper state
A fluorescence spectrum (b) shows a structure characteristic of the lower state, displaced to lower energies (mirror image of the absorption)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 53
Chlorophyllfluorescence
spectral emission
Source
Sample
Detector
Effects offluorescence
on vegetationreflectance
2-5 %20-30 %
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 54
Turbid Geometric Hybrid Ray Tracing
++++++++(+)+Process time
+++++++++++Accuracy++++++++(+)+Parameters
Ray TracingHybridGeometricTurbidModelsCriteria
Turbid models are the most convenient to invert actual remote sensing data
Ray-tracing models are the most realisting for detailed forward modelling
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 55
NOTE:
- Geometric models are not adequate when dealing with multiplescatering (i.e., in the near infrared)
- Ray-tracing models are usually not-invertible (too realisticrequire many input variables)
- The detailed geometric representation of the scene can beused as statistical representation (one case from the manypossible)
- Hibrid models tend to be used in a wrong way (inconsistenttreatment of first-order and multiple-order scattering)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 56
Accounting for varyingillumination conditions(direct versus diffuse light)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 57
Gap FractionP(θ,ϕ)
⎟⎠⎞⎜
⎝⎛ −
Δ
⎥⎦⎤
⎢⎣⎡
μΔϕθϕθλ−⎥⎦
⎤⎢⎣⎡
μΔϕθ−=ϕθ
1
0),(),(1.),(1),(
LLAI
LGLGP
Markov Gap Fraction ModelAdjustmentλ(θ) and ΔL
⎥⎦⎤
⎢⎣⎡
μϕθ−=ϕθ ),(),(0
LAIGExpP
Poisson Gap Fraction Model ( Turbid RTM)
Comparisonμ=cos(θ)
Solar beam
ΔL
Vegetation
Soil
λ(θ,ϕ)
•λ=1 ‘random structure’
•λ>1 ‘regular structure’
•λ<1 ‘clumped structure’
Canopy Gap Fraction Models
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 58
Rochdi and Baret
LAI=0.5
LAI=1
LAI=3
LAI=5
Gap fraction as a function of the viewzenith angle and theleaf-stem translation factor (χ) for smallleaves
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 59
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
400 700 1000 1300 1600 1900 2200 2500
lea f r e f le ct a nce
le af t r ansmi t t a nce
lea f absor pt a nce
so il r ef le ct a nce
wavelength (nm)
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0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
400 700 1000 1300 1600 1900 2200 2500
soil directcanopy directmult iple scat teringtotal ref lectance
wavelength (nm)
refle
ctan
ce
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 61
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Sun
CHANGES IN LEAF SIZEλ = 675 nm
refle
ctan
ce
view angle-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90
0 .0 2
0 .0 6
0 .0 4
0 .0 8
0 .1 0
0 .1 2
0 .1 4
0 .1 6
0 .1 8
0 .2 0
leaf size values
(m)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 62
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.080 .2
0 .4
0 .6
0 .8
1 .0
1 .2
1 .4
1 .6
2 .0
1 .8
Sunλ = 675 nm
CHANGES IN CANOPY HEIGHT canopy height values
(m)
view angle-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90
refle
ctan
ce
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 63
forward scatteringbackward scattering
MODTRAN 4(650 nm)
maximum
maximum
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 64
Leaf versuscanopy level
What gives a measurable signal?(LAI x fc) x Cab(LAI x fc) and Cab separately(LAI) and (fc) and Cab separately
All the curves correspondto the sametotal (canopy) Cab !!!
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 65
Non photosyntheticelements:spectral variability
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 66
SURFACE MODEL PARAMETERISATION:(a) Leaf inputs:
- Leaf effective thickness - Leaf water content- Total leaf chlorophyll (a+b) - Specific leaf weight- Ratio Ca/Cb - Leaf cellulose content- Fraction of Ca in LHCP - Leaf lignin content- Leaf carotenes content
(b) Canopy inputs:- LAI- fCover- Clumping parameter (H/D)
(c) Soil inputs:- Soil wetness parameter
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 67
Although there aredifficulties in modellingsome details in thespectral / angularvariability,current modellingcapabilities allowa quite precisereconstruction ofmeausuredspectralreflectance
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 68
- Cab = 50.17 μg/cm-2- LAI = 3.73- Cw = 0.0145 g/cm-2- Cm = 0.0036 g/cm-2- N = 1.5
CHRIS/PROBA – SAIL/PROSPECT (14/07/03)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 69
Information retrievalbased on optimised
spectral indices
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 72
Visible Atmospherically Resistant Index
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 73
Verhoef and Bach, 2007
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 76
Retrievals from hyperspectral data:
Canopy watery = 3,1431x + 857,
R2 = 0,5797
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
-100 0 100 200 300 400 500 600 700 800 900
Index
Corn bar1_12Subarbeet_bar1_12Barley bar1_12Wheat_bar1_12Alfalfa bar_1_12Corn bar2_12Sugarbeet bar2_12Barley bar2_12wheat bar2_12Alfalfa bar2_12Sugarbeet bar1_9Alfalfa bar1_9Sugarbeet bar2_9Alfalfa bar2_9Sugarbeet bar1_15Alfalfa bar1_15Subarbeet bar2_15Alfalfa bar2_15
y = 0,0359x + 0,347R2 = 0,3789
0
5
10
15
20
25
30
35
40
45
-100 0 100 200 300 400 500 600 700 800 900
Index
Corn bar1_12Sugarbeet bar1_1Barley bar1_12Wheat bar1_12Alfalfa bar1_12Corn bar2_12Sugarbeet bar2_1Barley bar2_12Wheat bar2_12Alfalfa bar2_12Sugarbeet bar1_9Alfalfa bar1_9Sugarbeet bar2_9Alfalfa bar2_9Sugarbeet bar1_1Alfalfa bar1_15Sugarbeet bar2_1Alfalfa bar2_15
Leaf chlorophyllLeaf chlorophyll
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 77
MERIS Terrestrial Chlorophyll Index (MTCI)
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0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
3 0 0 5 0 0 7 0 0 9 0 0 1 1 0 0 1 3 0 0 1 5 0 0 1 7 0 0 1 9 0 0 2 1 0 0 2 3 0 0 2 5 0 0
L A I= 4
λ (n m)
ρ
0 .0 0 1
0 .0 0 2
0 .0 0 4
0 .0 0 6
0 .0 1 0
0 .0 1 5
0 .0 2 0
0 .0 4 0
0 .0 6 0
0 .0 8 0
0 .1 0 0
LeafWat er
Co nt ent (cm )
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 79
0 .0 0 0 .0 1 0 .0 2 0 .0 3 0 .0 4 0 .0 5 0 .0 6 0 .0 7 0 .0 8 0 .0 9 0 .1 0
LAI=1LAI=4
leaf liquid water (cm)
0 . 5 0
0 . 5 5
0 . 6 0
0 . 6 5
0 . 7 0
0 . 7 5
0 . 8 0
0 . 8 5
0 . 9 0
0 . 9 5
1 . 0 0
refle
ctan
ce ra
tio
Results for AVIRIS channel 63 (968.2 nm)
0 .0 0 0 .0 1 0 .0 2 0 .0 3 0 .0 4 0 .0 5 0 .0 6 0 .0 7 0 .0 8 0 .0 9 0 .1 00 . 2 5
0 . 3 0
0 . 3 5
0 . 4 0
0 . 4 5
0 . 5 0
0 . 5 5
0 . 6 0
LAI=1LAI=4
leaf liquid water (cm)
ρ
Simplified method:modelling of liquid water absorption depth
as direct retrieval by a simple method
as first-guess for more sophisticate algorithms
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 80
Information retrievalbased on optimised
model-inversion techniques
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 81
• turbid medium• separation green / senescent parts: LAIT = LAIG + LAIS
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 82
The problem of model inversion can be considered fromdifferent perspectives:
(a) Root finding of a given function
(b) Solving non-linear set of equations
(c) Function minimisation
(d) Non-linear least-squares modeling of data
Root finding and solving non-linear set of equations wouldrequire that the function is “exact”, and for this reason function minimisation is normally preferred.
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 83
Choice of the merit function
Assumptions:
Incorporation of the uncertainties in the inverse process:
[ ] [ ] [ ] [ ]pt
pCRMmest
CRMmes VVCVVVRRWVRR −−+−−= −− 112 )()(χ
The maximum likelihood on the variables minimize:
Residuals
a prioriCovariance Matrix
Residuals
a prioriCovariance Matrix
Radiometric Part Variable Part
Assumptions: Gaussian distribution of the uncertainties
2)(∑ ⎥⎦⎤
⎢⎣⎡ −M
iiR
iCRM
imes VRRσ
2
∑ ⎥⎦⎤
⎢⎣⎡ −N
jiV
ipi VVσif diagonal matrixes
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 84
THE SHAPE OF THE MERIT FUNCTION
Absolute minimum at first guess (most probable value)
(location of minimum is variable)
X0
Xmin X max
maximum range of possible values
Ymin
Yref
Ymax
maximum range of probable values
Xminref Xmax
ref
f (X)non valid solutions
valid solution
absolute minimum
relative minimum
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 85
Neuralnetworkmethods
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 86
TrainingTheNeuralNetwork
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 87
approximations
Simplified model inversion(spectral fitting over restricted spectral ranges)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 88
Spectral-angular synergy:
application of synergy
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
0
5
10
15
20
25
30
35
40
45
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
SUGAR BEET
wavelength (nm)
wavelength (nm)
refle
ctan
cere
flect
ance
ALFALFA
Li ne 1 , n oo nLi ne 2 , n oo nLi ne 1 , mo rn in gLi ne 2 , mo rn in gLi ne 1 , a f t e rn oo nLi ne 2 , a f t e rn oo n
SV6 - C03 UTM-X: 577783 UTM-Y: 4324794 LAI = 1.71 fCover = 0.61
Li ne 1 , n oo nLi ne 2 , n oo nLi ne 1 , mo rn in gLi ne 2 , mo rn in gLi ne 1 , a f t e rn oo nLi ne 2 , a f t e rn oo n
V16 - C01 UTM-X: 577550 UTM-Y: 4324069 LAI = 1.84 fCover = 0.95
Homogeneous vegetation:Spectral information dominant
Heterogeneous vegetation:Angular information dominant
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 89
RETRIEVAL OF fCOVER(simplified)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 90
RETRIEVAL OF LEAF AREA INDEX (LAI)
- Difficult field measurements (for a given definition)- Variability in ground measurements
- LAI is retrieved linked to some absorber component:* chlorophyll* water* dry matter
- Coupled to fractional cover (view angle dependent)
- Difficult independent validation of LAI(coupled to other variables)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 91
700 900 1100
λ (nm)700 900 1100
λ (nm)
LAI = 1 LAI = 40.6
0.5
0.4
0.3
0.2
0.1
0.0
ρ
0.6
0.5
0.4
0.3
0.2
0.1
0.0
ρ
Line-fittinggives LAI aslinked to theslope of theedge of theabsorption
feature
Unique capability ofhigh spectral
resolution data
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 92
Inter-channel radiometric calibrationInstrumental spectral shiftsRadiometric noisesNatural intra-species variability (LAI changes)Natural spectral shifts (substrate bonds)Natural inter-species variability...
ADVANTAGES OF UNIFORMCONTIGUOUS SPECTRAL COVERAGE
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 93
- based on liquid waterabsorption properties
- accounting for canopygeometry effects
RETRIEVAL OF LEAF WATER CONTENT
non-linear fitting algorithm over a limited spectral range
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 94
line-fitting algorithm(non linear)over the 860 nm - 1320 nmlimited spectral rangeaccounting only forleaf water absorption
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 95
Decoupling of atmospheric effectswhen retrieving leaf / canopyinformation
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 96
[ 860 , 1320 ] nm
[ 1070 , 1320 ] nm[ 860 , 1080 ] nm
DAISEX-2000HYMAP data
atmosphericover-correction
potentialspectral shift
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 97
WATER VAPOURSPATIAL VARIABILITY
- very high spatial resolution watervapour maps (turbulence structure)
Residual atmosphericwater vapour effectson DAISEX-HYMAP data
1 .1 01 .0 51 .0 00 .9 50 .9 00 .8 5
atmospheric water vapour
surface liquid water
tran
smitt
ance
& re
flect
ance
(r
elat
ive
units
)
wavelength ( m)μ
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 98
“depth” is not always the bestindicator to determineamount of absorber by measuringreflectance acrossan absorption band
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 99
Influence of pigmentsdistribution within the leaf(for a given total leafchlorophyll content)
- potential separabilityof chlorophyll a and b
RETRIEVAL OF LEAF CHLOROPHYLL CONTENTline-fitting algorithm (non linear) over the 530 nm - 740 nm limitedspectral range accounting only for chlorophyll absorption
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 100
High modelling variability !
Are all pigmentsseparable in the signal ?
Key issues:- existing model parameterisationsdo not account for the observedvariability
- high variability set limits to thepossible decomposition of effectsdue to different pigments
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 101
[ 530 , 740 ] nm
restricted spectral interval(to improve in cases withvery low chlorophyll content)
optimum spectral interval
ROSIS data
HYMAP data
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 102
Retrievals from multi-angular data:Improvements in retrievals of:- LAI- fractional cover
Retrievals ofnew variables:- leaf size (d)- canopy heigh (h)
Compensationof angular effectsin other datasets
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 103
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 104
Weiss et al., 2001
Couplingcanopyfunctioningand radiativetransfermodels forremote sensing data assimilation
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 105
- Succesful results, intensively validated by means ofsimultaneous ground measurements of all relevantsoil / canopy / atmosphere properties
- Difficulties in retrieving biophysical variables whenconstraining models in all (most significant) inputsvariables: the accuracy in the retrieval of someparticular variable should not be compromisedat the expenses of wrong values for other key variables
- Use of multiple views to constraint LAI and fCoverretrievals (coupling of biochemicals andcanopy structure): alternative formulations allow toretrieve LAI = f(view angle) and then use such functionto derive structural properties (perticularly for the caseof suboptimal sampling of surface BRDF)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 106
Advanced retrievaltechniques:
multi-sptep procedures
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 107
spectral channels
multi-resolution spatial classification
homogeneity test
convergence test
N N+1
heterogeneous pixels: mixture model inversion
homogeneous pixels: reflectance model inversion
Nth iteration parameters
a iN{ }
i = 1 , ... , P
FIR
ST S
TEP
unmixing procedure
end-members parametric
characterisation
end-members definition (soil, vegetation, shade...)
preliminary values from empirical relationships
N=0
heterogeneous pixel masking
SEC
ON
D S
TEP
THIR
D S
TEP
additional outputs
generation of a complete output map for each variable(merge homogeneous + heterogeneous pixel outputs)
A general multi-step procedure
- Explicit separation of almostpure pixels from spectral mixtures
- Use of several retrieval techniquesfor each step
- Produce different adequate outputsfor each retrieval procedure
Such methods are used in practice for
real images
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 108
MODIS LAI/FPAR Retrievals: Main and Back-Up Algorithms
Main algorithm: during retrievals, surface reflectances predicted by RT model are compared with MODIS channel data (Red and NIR) and when agree corresponding LAI and FPAR are retrieved. The RT simulations are performed with the Stochastic RT model which accounts for 3D effects of vegetation heterogeneity with pair-correlation function. RT simulations are parameterized with vegetation type, leaf optical properties, soil reflectance patterns. Main algorithm delivers most accurate retrievals, based on best quality input.
Back-Up algorithm: If Main algorithm fails due to input (or RT model) uncertainties, the back-up algorithm retrieves LAI/FPAR from NDVI. Those are low accuracy retrievals, based on low precision input.
Main algorithm Back-Up algorithm
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 109
Validation of retrievals
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 110
Intensive field measurements
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 111
Ground measurements: Mean values
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
4,0
V29 V20 V14B V1
Individu al samples
0
0 ,5
1
1 ,5
2
2 ,5
3
3 ,5
4
4 ,5
5
- vegetation properties- soil properties- solar radiation- atmospheric status- surface fluxes
spatial variability
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 112
- Statistical representativity of measurements used for validation
- Strategy for spatial and temporal sampling
- Validation methodology versus retrieval technique
- Statistical extrapolation of results (sample versus population)
- Adaptation of different methodology for each biophysical parameter
- Examination of results in view of the expected limitations
- Adaptability to the application
- Critical review of actual achievements
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 113
0%
2%
4%
6%
8%
10%
12%
0.6
0.9
1.2
1.5
1.8
2.1
2.4
2.7 3
3.3
3.6
3.9
4.2
4.5
4.8
5.1
5.4
5.7 6
6.3
LAI
avg = 3.07; std = 1.45
Many types of crops:alfalfacornsugarbeetonionsgarlicpotato....
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
LAI Alfalfa Corn Sugarbeet
LAI measurements
113 Elementary Sampling Units (24 data samples each ESU)
covering the full LAI range
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 114
FRACTIONAL VEGETATION COVER
CROP Chlorophyll mean value per crop (mg*cm-2) FVC mean value per crop
C1 51 0.63 B, B3 44 0.94 On-1 20 0.64 G1 14.5 0.12 P1 35.5 0.96
Mean values per crops
Hemispherical Photographs (+) for LAI and FVC estimation
0
0,2
0,4
0,6
0,8
1
A9 A1 A10 P1 C2 C1 C3 C G1 B3 B ON1SV Vine GS
FVC_HP_A9FVC_HP_A1FVC_HP_A10FVC_HP_P1
FVC_HP_C2FVC_HP_C1FVC_HP_C3FVC_HP_C
FVC_HP_G1FVC_HP_B3FVC_HP_BFVC_HP_ON1
FVC_HP_SVFVC_HP_VINEFVC_HP_PAFVC_HP_GRASS
Fraction of Vegetation Cover (FVC)
0.96
0.63
0.94
0.64
0.12
C R O P F IELD S ESU s C orn C 3,C 2,C 1,C 9
A lfa lfa A9,A10,A1 15 Sugarbeet B3,B 6
O nions O N 1 2 G arlic G 1 3 Potato P1 6
Vineyard V 3 Papaver PA 1
G rass G rass 4 Fruit trees 2 4
Sparse Vegetation SV 2 TO TA L 18 55
ESUs description
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 115
Sources of problems:
1. image data quality (calibration, noises, pre-processing)
2. ground data quality
3. simplifications in theoretical models
4. adequacy of retrieval methods
All at the same level of importance ?
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 116
Scaling issues
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 117
Up-
scal
ing
Dow
n-sc
alin
g
Simultaneous NOAA AVHRR / Meteosat data composite (Iberian Peninsula)
Landsat TM data (La Mancha)
AVIRIS data (Barrax)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 118
RESAMPLING OF MULTI-RESOLUTION DATA
- Scaling analysis requires co-registrationof multi-resolution data
- Co-registration of multiple angular viewsrequires to handle multi-resolutionresampling techniques
- BRDF reconstruction with differentpixel size for each view angle requirescompensation of varying GFOV
- Details are specific for each sensor, but a generic procedure is possible
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 119
PIXELS GEOMETRY
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 120
(36x36 matrix)(1x36 vector)
RESAMPLINGMETHOD
(1x36 vector)
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 121
Spatial DegradationOriginalLANDSAT TM data
Optimum Interpolation
NOAA AVHRR dataOriginal
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 122
MERIS FR data
CHRIS/PROBA data
SCALINGASPECTS
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3 September 2007 D1La5 Advanced Optical – Retrieval of Information Jose Moreno 123
= 500 mσ
= 250 mσ
= 750 mσ = 1000 mσ
= 100 mσOriginal
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NDVI (NDVI)σ(Averaged)
0.0 0.2 0.3 0.40.1
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0.00
0.05
0.10
0.15
0.20
0.25
0.30
NDVI (Averaged)
(NDVI)σ
Scaling issues require the use of statistical techniques (spatialstatistics) to quantify the observed effects beyond thequalitative interpretation of differences
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VIS/NIR/SWIR Colour Composite Thermal data
Multiresolution data
1.25 m
3.75 m
12.0 m
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