A1258690354mzibv - PowerPoint PPT Presentation

Title: A1258690354mzibv


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Assessing the Impact of Structural Effects on the
Radiative Signature of Vegetation J-L.
Widlowski, B. Pinty, T. Lavergne, N. Gobron and
M. Verstraete Methods in Transport Workshop,
11th 16th September 2004, Granlibakken, USA
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Overview

• Origin of 3-D signatures in reflectance fields
• Implications for 1-D RT model inversions
• Spatial resolution limits for pixel-based
  inversion
• Using photon spreading to speed-up MC
  simulations of reflectance fields

• Origin of 3-D signatures in reflectance fields
• Implications for 1-D RT model inversions
• Spatial resolution limits for pixel-based
  inversion
• Using photon spreading to speed-up MC
  simulations of reflectance fields

3
Radiation Transfer in Vegetation Canopies

• conditioned by two important boundary conditions

At the top of the canopy, zTOC

• Impinging radiation has a
• direct and a diffuse component due to
  atmospheric scattering

Ref Govaerts, PhD Thesis, 1996
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Leaf Optical Properties

• Leaf reflection and transmission depend
  primarily on wavelength, plant species, growth
  condition, age and position in canopy.

Ref Govaerts et al. (1995) IEEE IGARS95
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Foliage Structural Properties III
Vegetation foliage features characteristic
leaf-normal distributions, g(OL) with preferred

• azimuthal orientations, g(fL)

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Foliage Structural Properties III
In vegetation canopies the extinction coefficient
is directionally variant but wavelength
independent.
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Foliage Structural Properties III
Extinction coefficient is wavelength independent,
but directionally variant.
For a volume of oriented, finite-sized scatterers
(1-D medium)
se(z, O, O0) ?(z) G(O) O(z, O, O0)

• Leaf area density m2 / m3

• Interception probability along O

• Enhanced return-probability near
  retro-reflection direction

Ref Pinty et al. (1997) JAS Knyazikhin et al.
(1998) JGR
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Tree Structural Properties

• Actual trees are very complex, featuring
• species-specific patterns of
• foliage distribution
• leaf orientation
• crown shape and dimensions
• branch trunk structures
• growth processes

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Canopy Structural Properties
Actual vegetation canopies include location-specif
ic
All of which have an impact on the
surface-leaving reflectance field.
Widlowski et al., 2003, EUR Report 20855
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Multi-directional surface observations
Different fractionsof soil and foliage
contribute to the surface-leavingradiation if
targetarea is observedfrom different viewing
angles
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Spectral Contrast between Vegetation Background
Leaf
soil
Reflectance
Wavelength
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BRF shapes of Heterogeneous Canopies NIR
Sparse
Dense
Medium
Ref Pinty et al. (2004) JGR-Atmosphere
(submitted)
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Spectral Contrast between Vegetation Background
RED
Soil back- scattering dominates over leaf
scattering in the red
Leaf
soil
Reflectance
Wavelength
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BRF shapes of Heterogeneous Canopies Red
Sparse
Dense
Medium
Ref Pinty et al. (2004) JGR-Atmosphere
(submitted)
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The RPV parametric model
BRF(z,O0 O) ?0 MI(k) FHG(T) H (?c)
?0 - controls amplitude level k - controls
bowl/bell shape T - controls forward/backward
scattering ?C - controls hot spot peak
Ref Rahman et al. (1993) JGR
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The RPV parametric model
BRF(z,O0 O) ?0 MI(k) FHG(T) H (?c)
?0 - controls amplitude level k - controls
bowl/bell shape T - controls forward/backward
scattering ?C - controls hot spot peak
Ref Rahman et al. (1993) JGR
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Impact of Canopy Structure on surface BRFs
SZA30o
?red
IFOV275 m
350 structurally different canopy architectures
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Impact of Canopy Structure on surface BRFs
Bell shape
1.5
kred
SZA30o
?red
1.0
IFOV275 m
0.5
Bowl shape
Ref Widlowski et al. (2004), in print, Climatic
Change
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Overview

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Spatial resolution limits for pixel-based
  inversion
• Using photon spreading to speed-up MC
  simulations of reflectance fields

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Spatial resolution limits for pixel-based
  inversion
• Using photon spreading to speed-up MC
  simulations of reflectance fields

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Matching surface BRFs with 1-D models
Assume you have a set of multi-directional
observations of a surface target and - in
absence of any a priori information regarding its
structure - wish to utilize a 1-D RT model to
retrieve information about that surface target.
Whats the impact of the structural differences
in both models?
Approach Use a large LUT (containing 47000
candidates) spanning the entire domain of
probable 1-D solutions, and find the best
matching candidate under identical conditions of
illumination and viewing.
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Matching surface BRFs with 1-D models
Find the 1-D surface that is best at mimicking
the reflectance anisotropy of a 3-D target.
Heterogeneous discrete canopy 3-D
Widlowski, 2001, PhD Thesis
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Matching surface BRFs with 1-D models
Find the 1-D surface that is best at mimicking
the reflectance anisotropy of a 3-D target.
?0 30o
Fitting criteria 7 BRF observations VZA 0, ?25,
?45 ,?60? ?red
Widlowski et al., 2004, JGR - submitted
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Matching surface BRFs with 1-D models
1-D canopies that perfectly fit the surface
leaving BRFs of a 3-D target may be very accurate
in predicting the albedo but not the canopy
absorption, transmission etc.
Ref Widlowski et al. (2004), JGR, submitted
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Impact of Canopy Structure on surface BRFs II
Bell shape
1.5
kred
1.0
0.5
Bowl shape
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Impact of Canopy Structure on surface BRFs II
Leaf area index (LAI)
increases
Ref Pinty et al. (2002) IEEE TGRS
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Impact of Canopy Structure on surface BRFs II
Ref Pinty et al. (2002) IEEE TGRS
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Impact of Canopy Structure on surface BRFs
350 forest scenes
In general, the shape of the reflectance
anisotropy of a pure 3-D target tends to be
different from that of its IPA or 1-D
homologue k3-D ? k1-D

A 1-D canopy having a quasi-identical
reflectance anisotropy shape as a 3-D target is
almost certainly not its homologue!
Widlowski et al., 2004, JGR, submitted
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Matching surface BRFs with 1-D models

• 3-D surface targets tend to exhibit enhanced
  bell-shaped BRF patterns wrt. their 1-D
  homologues
• higher nadir BRFs
• lower BRFs at large VZA

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Matching surface BRFs with 1-D models

• 1-D canopy capable of mimicking BRFs of 3-D
  target consequently has
• enhanced soil albedo, a1D
• reduced LAI (as LAI3D increases)
• reduced single scattering
• albedo, ?1D (as LAI3Dincreases)
• increase leaf interception at large VZA (as
  LAI3Dincreases)

Ref Widlowski et al. (2004), JGR, submitted
30
Matching surface BRFs with 1-D models

• 1-D canopy capable of mimicking BRFs of 3-D
  target consequently has
• enhanced soil albedo, a1D
• reduced LAI (as LAI3D increases)
• reduced single scattering
• albedo, ?1D (as LAI3Dincreases)
• increase leaf interception at large VZA (as
  LAI3Dincreases)

1-D leaf normal distribution
Ref Widlowski et al. (2004), JGR, submitted
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Matching surface BRFs with 1-D models
The state variables of a 1-D canopy that is
capable of mimicking the reflectance anisotropy
of a 3-D target have to be interpreted
cautiously to account for 1) the structural
differences with the 3-D target, and 2) the lack
of information regarding canopy absorption
transmission.
Ref Widlowski et al. (2004), JGR, submitted
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Overview

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Pure 1D approach requires further interpretation
  of state variables
• Given 3-D structure effective state variables can
  be found for 1-D
• Spatial resolution limits for pixel-based
  inversion
• Using photon spreading to speed-up MC
  simulations of reflectance fields

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Pure 1D approach requires further interpretation
  of state variables
• Given 3-D structure effective state variables can
  be found for 1-D
• Spatial resolution limits for pixel-based
  inversion
• Using photon spreading to speed-up MC
  simulations of reflectance fields

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Spatial resolution limit

• RT model based interpretation of multi-angular
  BRF measurements of individual pixels is limited
  to spatial resolutions where net horizontal
  fluxes are close to zero radiatively independent
  volume

• What are the typical distances that photons
  travel laterally in between their points of entry
  and exit at the top of the canopy?

• At what spatial resolution do horizontal fluxes
  affect pixel-based model inversions?

34
Horizontal divergence of radiation

• What are the typical distances that photons
  travel between their points of entry and exit at
  the top of the canopy?

Red
NIR
Widlowski et al., 2004, JGR, submitted
35
Horizontal divergence of radiation

• What are the typical distances that photons
  travel between their points of entry and exit at
  the top of the canopy?

• canopy structure controls extinction
  coefficient and the most likely distance, d

Red - NIR

• multiple-scattering makes photons in NIR travel
  longer distances than in red

• 0.5 (1 ) of all photons in red (NIR) have d
  lt 100m

Widlowski et al., 2004, JGR, submitted
36
Assessment of Horizontal Fluxes

• What are the typical flux quantities that
  travel through the lateral sides of some canopy
  volume, V at a spatial resolution, S?

f0
O0
f0
zTOC
V
S
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Magnitude of Net Horizontal Flux Components
Red
Maximum minimum flux across the
lateral sides of voxel that are
perpendicular to f0
?0 0o, 15o, 30o, 55o
3D forest with 300 stem/ha
Widlowski et al., 2004, JGR, submitted
38
Magnitude of Net Horizontal Flux Components
Red
Maximum minimum flux across the
lateral sides of voxel that are
perpendicular to f0
Maximum minimum flux across the
lateral sides of voxel that are parallel
to f0
ve values ? more photons enter
voxel than exit
-ve values ? more photons exit voxel
than enter
?0 0o, 15o, 30o, 55o
3D forest with 300 stem/ha
Widlowski et al., 2004, JGR, submitted
39
Magnitude of Total Net Horizontal Flux
?0 30o
?0 60o
maximum and minimum net horizontal
flux into voxel
maximum and minimum net horizontal
flux into voxel
ve values ? more photons enter
voxel than exit
? NIR, Red
-ve values ? more photons exit voxel
than enter
3D forest with 300 stem/ha
Widlowski et al., 2004, JGR, submitted
40
Impact of Net Horizontal Fluxes
Depends on magnitude ofsurface-leaving radiation!
Red
?0 30o
31m
18m
Since ?FHor is larger in red than NIR, and F?
larger inNIR than red look at red
5
For sensor with BRF accuracy of 5 in red
spatial resolution gt 31 mrequired for
pixel-basedBRF interpretation
-5
29m
18m
Tree density 300, 600, 1200, 1800 stem/ha
Widlowski et al., 2004, JGR, submitted
41
Overview

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Pure 1D approach requires interpretation of
  state variables
• Given 3-D structure effective state variables can
  be found for 1-D
• Spatial resolution limits for pixel-based
  inversion
• Stay above 30 m for 5 sensor accuracy
• Using photon spreading to speed-up MC
  simulations of reflectance fields

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Pure 1D approach requires interpretation of
  state variables
• Given 3-D structure effective state variables can
  be found for 1-D
• Spatial resolution limits for pixel-based
  inversion
• Stay above 30 m for 5 sensor accuracy
• Using photon spreading to speed-up MC
  simulations of reflectance fields

42
Raytran a 3-D Monte Carlo ray-tracing model

• Raytran describes the radiation transfer on a
  ray-by-ray basis, following individual
  ray-trajectories from their source through all
  relevant interactions until an eventual
  absorption or exiting from the simulated scene
  occurs.
• Information is subsequently extracted from
  ray paths BRFi pNi / N?Oi

Ref Govaerts (1996) EU Report 16394 EN
43
Improving the speed of the Raytran model
Only 7 (18 ) of injected rays in the red (in
NIR) contribute towards estimation of surface
albedo substantially less for individual BRFs.

• Enhance the contribution of individual photons in
  Raytran model via the photon spreading variance
  reduction technique
• Ross Marshak, 1988
• Calculation of Canopy Bidirectional Reflectance
  Using the Monte Carlo Method
• absorption is probabilistic (photons carry
  weights)
• fictitious flight towards detectors yields BRF
• Thompson Goel, 1998
• Two Models for Rapidly Calculating Bidirectional
  Reflectance of Complex Vegetation Scenes Photon
  Spread (PS) model and Statistical Photon Spread
  (SPS) Model
• absorption is deterministic (Monte Carlo
  scheme)
• photon spreading towards detectors yields BRF

44
Developing the Rayspread model
Principle of Rayspread.
At each physical interaction in the main ray
path, a secondary spreading ray is aimed at
each sensor. The probability of reaching the
detector without physical interactions is
calculated and added to its radiance counter.
45
Developing the Rayspread model
Principle of Rayspread.
At each physical interaction in the main ray
path, a secondary spreading ray is aimed at
each sensor. The probability of reaching the
detector without physical interactions is
calculated and added to its radiance counter.
P3
P2
P4
P1
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Developing the Rayspread model
Principle of Rayspread.
At each physical interaction in the main ray
path, a secondary spreading ray is aimed at
each sensor. The probability of reaching the
detector without physical interactions is
calculated and added to its radiance counter.
P3
P2
P4
P1
P5
Each sensor has already 2 (1) contribution(s)
47
Developing the Rayspread model
Principle of Rayspread.
At each physical interaction in the main ray
path, a secondary spreading ray is aimed at
each sensor. The probability of reaching the
detector without physical interactions is
calculated and added to its radiance counter.
Pr(x,y,z,q0,f0d) Prsurf.Refl.(q0,f0q1,f1)
Prtravel(x,y,zd)
Prsurf. Refl.(q0,f0q1,f1) Lambertian, specular,
etc.
n
q0
q1
x,y,z
48
Developing the Rayspread model
Principle of Rayspread.
At each physical interaction in the main ray
path, a secondary spreading ray is aimed at
each sensor. The probability of reaching the
detector without physical interactions is
calculated and added to its radiance counter.
Pr(x,y,z,q0,f0d) Prsurf.Refl.(q0,f0q1,f1)
Prtravel(x,y,zd)
Prsurf. Refl.(q0,f0q1,f1) Lambertian, specular,
etc.
n
d
q0
d
q1
l
M
v
x,y,z
Prtravel(x,y,zd)0
Prtravel(x,y,zd)f(l,v,M)
49
Developing the Rayspread model
Principle of Rayspread.
At each physical interaction in the main ray
path, a secondary spreading ray is aimed at
each sensor. The probability of reaching the
detector without physical interactions is
calculated and added to its radiance counter.
On the sensors side
50
Developing the Rayspread model
50mx50m forest scene. 250 trees. 153000 objects

Raytran 400 million rays TNIR 16h20
(980mn) TRED 8h24 (504mn)
Rayspread 50,000 rays TNIR 15mn TRED 10mn
51
Developing the Rayspread model
RAdiation transfer Model Intercomparison exercise
(RAMI)
Mean-0.01

• Rayspread Linux Cluster
• 10 nodes (PIII 450 / 380MB Ram)
• 52 RAMI Homogeneous
• (Turbid and Discrete) experiments.

Speed-up roughly 100
52
Overview

• Origin of 3-D signatures in reflectance fields
• hot spot effect leaf/tree gap sizes, spectral
  contrast of soil/canopy
• bowl/bell shape leaf/tree distribution, spectral
  contrast of soil/canopy
• Implications for 1-D RT model inversions
• Pure 1D approach requires interpretation of
  state variables
• Given 3-D structure effective state variables can
  be found for 1-D
• Spatial resolution limits for pixel-based
  inversion
• Stay above 30 m for 5 sensor accuracy
• Using photon spreading to speed-up MC
  simulations of reflectance fields
• Speed-up by a factor of 100

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THANK YOU!