G5-1-2 - bilişim teknolojileri enstitüsü

Transkript

01.07.2014
UYGU 2104 – Uydu Yer Gözlem Uygulamaları Yaz Okulu
TÜBİTAK
Microwave Remote Sensing of
Forest and Agriculture
Mehmet Kurum
TÜBİTAK BİLGEM
Bilişim Teknolojileri Enstitüsü
FRI.AM1
Uydu Yer Gözlem Uygulamaları Yaz Okulu | 23-27 Haziran 2014 | T ÜBİTAK Gebze Yerleşkesi
Content
UYGU 2014
TÜBİTAK
Objectives
Motivation
Agriculture
Forest
SAR Techniques
System and Target Paremeters
Vegetation Modeling
Active
Passive
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Educational Objectives
UYGU 2014
TÜBİTAK
To learn how microwave data are usable for
vegetation applications.
To understand advantages of microwave
techniques over traditional measuring techniques
and optical systems
To understand the limitations of microwave
techniques
To learn optimal sensor and acquisition
parameters for vegetation applications
Introduction - Agriculture
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The negative impacts of current agriculture are manifold
and can be related to either agricultural expansion or
intensification
biodiversity is threatened by land clearing and habitat
fragmentation
greenhouse gas emissions from land clearing, crop production
and fertilization contribute already to 1/3 of global GHG
emissions
global nitrogen and phosphorus cycles have been disrupted,
with impacts on water quality aquatic ecosystems and marine
fisheries
freshwater resources are depleted as nearly 80 percent of
freshwater currently used by humans is for irrigation
[Atzberger, Remote Sensing of Environment, 2013]
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Remote sensing for agriculture
UYGU 2014
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A tool for management and optimization of resources
End users
Demands
Objective
Crop-type mapping and
classification
Justification of subsidies and fraud
detection
Water resources
Authorities or
government agencies consumption
Farmers with
extensive fields
Control in regions suffering
droughts or with scare water
resources
Yield prediction
Economic and market predictions,
price regulations, etc.
Timely information
about crop condition
Planning and triggering farming
practices according to specific
phonological stages
Water requirements
Irrigation only when and where
necessary
Final crop productivity
Benefits
Forest - Did you Know?
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Forests cover approximately 33% of the Earth’s land surface
[JENSEN, 2000]
Forests play an important role in the global carbon cycle, since each
year forests absorb approximately 1/12 of the Earth’s atmospheric
CO2 stock [MALHI et al., 2002]
Forested ecosystems account for app. 72% of the Earth’s terrestrial
carbon storage [MALHI et al., 2002]
Therefore, Vegetation biomass is a larger global store of carbon
than the atmosphere [FAO, 2009]
Between 1850 and 2011, humans have released app. 480 Gt (480
BILLION TONS!!!) of CO2 into the atmosphere through fossil fuel
burning and land use changes (e.g. deforestation and fires)
[GHASEMI et al., 2011]
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Main components of biomass distribution
UYGU 2014
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Stem Biomass is strongly related to the
commercially interesting biomass.
Applications of RADAR to Forest
mapping/monitoring
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UYGU 2014
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Mapping forest/land cover
Mapping wetlands (inundated/flooded versus non-flooded)
Mapping structural attributes (height, basal area, biomass,
volume)
Monitoring disturbance (logging, fire, windthrow, insect
damage)
Monitoring change (deforestation, degradation, reforestation)
Monitoring photosynthetic processes (growing-season
length)
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SAR Techniques
UYGU 2014
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Backscatter analysis (wavelength, polarization, incidence angle,
number of images).
Interferometry: Coherence analysis (wavelength, polarization,
incidence angle, temporal and spatial baseline, number of images,
acquisition conditions)
Interferometry: Phase analysis (wavelength, incidence angle, high
coherence required, acquisition conditions)
Polarimetry (wavelength, incidence angle, number of images)
Polarimetric Interferometry (wavelength, polarisation, incidence
angle, temporal and spatial baseline)
SAR (Polarimetric) Tomography (wavelength, polarisation,
incidence angle, spatial baseline, high coherence required, number of
images)
Linking SAR measures with vegetation parameters
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Forward
SAR Measures:
• Backscatter
• Interferometry
• Polarimetry
Model
Inversion
System Paremters (Sensor)
• Wavelength/Frequency (X, C, L,
and P bands)
• Polarization (HH, VV, and HV)
• Incidence angle
• Resolution
Vegetation Parameters:
• Height
• Biomass
• Type
• Health
• Etc.
Target Paremters (Ground)
• Structure (size, orientation, and
distribution of scattering surfaces)
• Surface roughness (relative to
wavelength)
• Dielectric constant (moisture content)
• Slope angle/orientation
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System Parameters: Frequency
UYGU 2014
TÜBİTAK
Vegetation targets are composed of water
Dielectric constant of water is a function of the frequency
(wavelength)
Real part of the dielectric constant of pure water vs. frequency for 0
and 20 C
[Ulaby et. Al. 1986]
Microwave Penetration into Canopy
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UYGU 2014
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Austrian pine
X band
λ= 3 cm
L band
λ= 27 cm
P band
λ= 70 cm
VHF
λ>3m
Radar signal related to biomass of leaves, branches, trunk,
depending on the SAR frequency band, polarization and incidence
[Le Toan]
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01.07.2014
GeoSAR
UYGU 2014
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System Parameters: Frequency
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UYGU 2014
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Decreasing frequency Signal penetration into vegetation/soil increases
Higher frequencies (e.g. X-Band) dominated by canopy scattering
Lower frequencies (e.g. L- or P-Band) dominant or significant soil
backscatter contributions
Sources of radar backscatter from cultivated crops
[Brisco & Brown 1998]
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System Parameters: Polarization
UYGU 2014
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[Lopez. 1983]
Target Parameters: Soil Parameters, Roughness
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How does roughness influence radar backscatter?
given a fixed incidence angle, signal strength of the reflected component
decreases and the strength of scattered component increases with increasing
surface roughness.
Schematic illustration of scattering from surfaces with different roughness conditions at
increasing (from a to c) incidence angles
[Woodhouse 2006]
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UYGU 2014
Target Parameters: Soil Parameters - Soil Moisture
TÜBİTAK
With increasing soil
moisture, the dielectric
constant of the soil is
increasing and thus leads
to an enhanced radar
backsatter also towards the
sensor.
Effect of soil moisture on backscattering behavior
Relationship between soil moisture and
dielectric constant
[Woodhouse 2006]
Target Parameters: Soil Parameters - Soil Moisture
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Winter wheat field
[Lievens & Verhoest, 2012]
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Target Parameters: Plant Parameter Biomass
UYGU 2014
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Rice Crop
[Le Toan]
Biomass vs. Stem Volume and Height
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UYGU 2014
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[Saatchi et. al.]
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01.07.2014
Sensitivity of SAR to Biomass
UYGU 2014
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Landes Forest, France
RAMSES
AirSAR
Carabas
[Le Taon et al.]
P-band Xpol Response
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1
[Le Taon, 2010]
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Interferometric Coherence
UYGU 2014
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Analysis of PALSAR Data
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PALSAR Coherence
UYGU 2014
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Power of Coherence
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01.07.2014
Content
UYGU 2014
TÜBİTAK
Microwave Vegetation Models
Radar Radiometer Response
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UYGU 2014
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Backscatter
(radar)
Emission
(radiometer)
Soil
RADIOMETER
RADAR
More sensitive to Soil Moisture
Poor resolution (~40km)
More stable relative to small
changes in surface and vegetation
parameters
Surface Roughness
Canopy Architecture
Higher resolution
Attenuation
Scattering
Properties of
Vegetation
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Geometry of the Problem
UYGU 2014
TÜBİTAK
Emission from a forest layer over a rough ground
Deciduous Trees
Conifer Trees
Geometrical Configuration for backscatter case
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UYGU 2014
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z
Antenna
Two layer vegetation model
Bθ
θi
n̂
Diffuse boundary
0
xt
d1
Crown
Layer
d2
Trunk
Layer
−d1
−d
Soil
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01.07.2014
Ground Measurements
UYGU 2014
TÜBİTAK
For the sake of all trees on Earth…
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Tree Destructive Sampling
UYGU 2014
TÜBİTAK
Detailed measurements of size/angle distributions of the tree constituents
(trunk, branches, and leaves), water content and dry biomass.
Canonical Shapes
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Finite Cylinder
Elliptical Disk
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Scattering from Canonical Shapes
UYGU 2014
TÜBİTAK
Scattered field
Mean
Green’s function
Kernel of
transition operator
Mean field
Scattering Pattern of a Dielectric Cylinder
35
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VV-pol
HH-pol
Vertical Trunk
VV-pol
HH-pol
Vertical Branch
VV-pol
HH-pol
Vertical Corn Stalk
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Simulation Setting – Corn Canopy
UYGU 2014
TÜBİTAK
Emission from a corn layer over a rough ground
Average Corn Canopy from June 20–25, 1998
25 (major)
2.5 (minor)
Leaf
Radius [cm]
z
Receiver
Dielectric Constant
= 15°
Air
Stalk
Angle Distribution [⁰]
Layer
z = −d
Ground
0 - 45 (uniform)
2.0
Length [cm]
100
Dielectric Constant
Leaf Density [# / plant]
Corn
35 + i10
Diameter [cm]
Angle Distribution [⁰]
Ground
z =0
0.3
Thickness [mm]
Stalk Density [# / m2]
50 + i15
0 - 15 (uniform)
10
9.4 (Full)
Layer Height [m]
1.5
VWC [kg/plant]
0.5
1.0
Rms Height [cm]
VSM Range
[m3
m-3]
0.14 − 0.25
Simulation Setting – Soybeans
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Soybeans [Du et al., tgrs 2000]
Emission from a soybean layer over a rough ground
Leaf
z
Soybean
z = −d
Ground
Stem
Air
Layer
z =0
0.26
Thickness [mm]
Dielectric Constant
30.8 + i9.4
Angle Distribution
plagiophile*
0.3
Diameter [cm]
Ground
Receiver
2.5
Radius [cm]
20
Length [cm]
Dielectric Constant
30.8 + i9.4
Angle Distribution
plagiophile*
Leaf Density [# / m2]
9794
Stem Density [# / m2]
999
Layer Height [cm]
60
VWC [kg / m2]
1.7
0.5
Rms Height [cm]
VSM Range
[m3
m-3]
*plagiophile = sin3 ,
3
4
0.10 − 0.35
0≤ ≤ Uydu Yer Gözlem Uygulamaları Yaz Okulu | 23-27 Haziran 2014 | T ÜBİTAK Gebze Yerleşkesi
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01.07.2014
Content
UYGU 2014
TÜBİTAK
Passive Microwave
Modelling of Vegetation
Radiative Transport (RT) Equations
39
UYGU 2014
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cos Up-welling:
Down-welling:−cos , =
Extinction
− , 4
2
% 2 ℑ45〈77 〉9
10
2
'( , ); ′, )′ =
, , = # Ω′ % &'( , ); ′ , ) ′ ( ′ , + '( , ); − ′, )′( − ′, +
2
(=h,v
, = − # Ω′ % '( , ); ′, )′
(=h,v
Phase Function:
+
Scattering Source Function:
Absorption Coefficient:
4
!"
+
Scattering
into the beam
− , = − − − , + − !" + − , Extinction Coefficient:
=
Emission
2
2 % 2 〈:7( , ); ′, )′: 〉
2
0 ≤ ≤ /2
0 ≤ ′ ≤ /2
∈ /h,v0
′
− ′
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01.07.2014
Rearranging RT Equations
in terms of Scattering Coefficients
UYGU 2014
TÜBİTAK
Substituting absorption coefficient and scattering source function into up-welling RT equation:
cos , = − , + !" ; − # Ω′ % '( , ); ′, )′<
4
(=h,v
+ # Ω′ % &'( , ); ′ , )′ ( ′ , + '( , ); − ′, )′( − ′, +
(=h,v
2
New Set of RT equations:
, cos = − , + !" + = , −cos − , = − − − , + !" − + = − , Difference Scattering Function due to difference between local specific
intensities and physical temperatures.
= , = # Ω′ % &'( , ); ′ , ) ′ >( ′ , − !" ?
2
(=h,v
+ '( , ); − ′, )′>( − ′, − !" ?+
Iterative Approaches:
Successive Orders of Scattering
• Zero Order RT Equations:
cos −cos 41
UYGU 2014
, = − , = 0
TÜBİTAK
@ , 0
= − @ , + !"
0
0
@ − , 0
= − − @ − , + − !"
• First Order RT Equations
−cos cos @ , 1
0
= − @ , + !" + , 1
@ − , 1
0
= − − @ − , + − !" + − , 1
Scattering Source Function Due to Zero-Order Emission
, = # Ω′ % 5'( , ); ′, )′@ ′, + '( , ); − ′, )′@ − ′, 9
0
2
(=h,v
0
0
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01.07.2014
Iterative Approaches:
Albedo Expansion
•
Zero Order RT Equations:
cos −cos •
UYGU 2014
= , = = − , = 0
TÜBİTAK
0
, 0
= − , + !" 0
− , 0
= − − − , + !" − First Order RT Equations:
cos −cos , 1
0
= − , + !" + = , 1
1
− , 1
0
= − − − , + !" − + = − , Difference Scattering Function
due to the zero-order local brightness and physical
temperatures
= , = # Ω′ % 5'( , ); ′, )′A( ′, − !" B + '( , ); − ′, )′A( − ′, − !" B9
0
2
(=h,v
0
0
Zero-Order RT Solutions:
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TÜBİTAK
Up-welling Emission Down-welling Emission From
Layer Reflected by Ground
From Layer
0
@ = >1 − C ?>1 − D ? + C EF >1 − C ?>1 − D ?
+ C >1 − EF ?
Ground Emission
Albedo Expansion
= 1 − G@H 0
Specular Albedo: The incident
power scattered by the ground in the
specular direction
G
@H
= C2 EF Uydu Yer Gözlem Uygulamaları Yaz Okulu | 23-27 Haziran 2014 | T ÜBİTAK Gebze Yerleşkesi
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Physical Parameters
UYGU 2014
TÜBİTAK
EF o Reflectivity
C o Transmissivity
Reflection from ground
Related to soil moisture &
surface roughness
Propagation within vegetation
Related to vegetation VWC &
architecture
= I − J @H D –
–
–
–
–
Fresnel Reflectivity
Rough Surface Scattering
h-parameterization
Forward Scattering Theorem
b-parameterization
o Single-Scattering Albedo
Fractional power scattered from
vegetation constituents
Related to vegetation VWC &
architecture
=
@
@ + – How to quantify
The First-Order RT Model
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UYGU 2014
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0
: Tau-omega model
1
0
1
= + Ω 1
Ω First-Order Scattering
= Tau-omega model
+ First-Order Scattering
ΩK 1
Ω=K 1
ΩL 1
Ω= 1
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01.07.2014
A bit of Math!
UYGU 2014
TÜBİTAK
Peake’s Approach
(Energy Conservation)
Active Problem
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UYGU 2014
TÜBİTAK
Passive Problem
2
Absorption Coefficient ∶ 2 = 1 − G where
G = G
G
@H
@H
+ GM77 = C2 EF Diffuse Albedo : the scattered portion of
the incident power from the layer into a
hemisphere above the layer.
G
M77
=
-Employ Kirchhoff’s principle to
equate the radiation emitted from a
surface to the radiation absorbed by
the surface.
= 2 = 1 − G 1
0 ′,
0 ′,
# Ω′ % &N(
)′; − , ) + N(
)′; , )
4 cos 2
(=h,v
0 0 + N(
− ′, )′; − , ) + N(
− ′, )′; , )+
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Comparison of Models
Branch (high albedo)
UYGU 2014
TÜBİTAK
M77
@H
= 1 − G
− G
1
a0:Zero-Order Albedo
a1:First-Order Albedo
s1:First-Order
Scattering
s0: Zero-Order
Scattering
Branch (Finite
Cylinder) Properties
Radios : 1.6 cm,
Length : 153.8 cm
Distribution:
Uniform 10º to 60º
Density : 0.15 #m-3
Dielectric : 12.0 + i2.9
Ground Properties
Flat (no roughness)
Volumetric Moisture
Content : 0.38 cm3 cm-
0
v
v
h
v
h
h
v
1
@ =
h
0
@ + Ω 3
Comparison of Models
Leaf (low albedo)
UYGU 2014
TÜBİTAK
M77
@H
= 1 − G
− G
1
a0:Zero-Order Albedo
a1:First-Order Albedo
s1:First-Order
Scattering
s0: Zero-Order
Scattering
Leaf (Circular Disk)
Properties
Radios : 10.5 cm,
Thickness : 0.12 mm
Distribution:
Uniform 0º to 90º
Density : 11.12 #m-3
Dielectric : 35.2 + i5.3
Ground Properties
Flat (no roughness)
Volumetric Moisture
Content : 0.38 cm3 cm-
49
0
@ = @ + Ω 1
0
3
50
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01.07.2014
The Deciduous Trees (Models vs. Data)
UYGU 2014
TÜBİTAK
VSM : 0.38 cm3 cm-3
rms height : 0.9 cm
Physical Model
(τ-ω-Ω):
@ = >1 − C2 E@ ? − D >1 + C E@ ?>1 − C ? + Ω 1
Effects of Foliation and Soil Moisture
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First-Order Scattering
April
No-Leaf Canopy
– changes in
– no changes in
–
– changes in
tree state
no change in
soil moisture
tree state
soil moisture
May
Full Canopy
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Measurement vs.
Vegetation RT models
UYGU 2014
TÜBİTAK
Corn field of full density
θ0=15⁰
Corn field of 2/3 density
θ0=15⁰
A0
A0
S1
S1
S0
S0
0.23
-
0.22
-
Measured Emissivity
−
Simulated Emissivity
=
1
∆
-
●
□
○
0.19
Non-Scattering Solution :
Zero-Order Solution
:
First-Order Solution
:
0.16
0.14
VSM m3 m-3
>1 − C2 EF ?
A0
0.24
-
0.22
-
0.19
-
0.17
- 0.15
VSM m3 m-3
− D >1 + C EF ?>1 − C ? +
Z 1
S0
S1
Content
53
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Active Microwave
Modelling of Vegetation
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Radar Response
UYGU 2014
Average Received Power
TÜBİTAK
Measured Transient Response from Trees to L-Band
Stepped-Frequency Radar (1.25 GHz)
The tree canopy and tree-ground effects appear at different times.
10dB
ComRAD
Ground
Time
30ns
Bistatic Scattering
from a Forested Terrain
UYGU 2014
TÜBİTAK
z
Transmit
Antenna
55
Receive
Antenna
Fop
Fiq
xo
nˆ i
xi
( ε 0 , µ0 )
nˆ o
xt
z=0
Forest
z = −d
( ε g , µ0 )
Ground
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Scattering from a single particle
embedded in the mean medium
UYGU 2014
TÜBİTAK
z
Transmit
Antenna
Receive
Antenna
Fop
ri
Fiq
nˆ o
xo
ro
nˆ i
xi
( ε 0 , µ0 )
xt
z=0
s
Effective
Medium
(
ε eff , µ0
Scatterer
S ( xα , yα , − zα )
)
z = −d
( ε g , µ0 )
Ground
Geometrical Configuration for backscatter case
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z
Two layer vegetation model
Antenna
Bθ
θi
Far field Conditions:
1. Each scatter is in the far-field of antenna.
2. Antenna is in the far-field of each scatterer.
n̂
Diffuse boundary
0
xt
d1
Crown
Layer
d2
Trunk
Layer
−d1
−d
Soi
l
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Scattering Mechanism
UYGU 2014
Direct Term
Reflected Term
Direct-Reflected
Term Type I
Direct-Reflected
Term Type II
TÜBİTAK
Individual Scattering Components
59
UYGU 2014
TÜBİTAK
Branch
Contribution
Trunk Contribution
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The Effect of Soil Moisture
UYGU 2014
TÜBİTAK
WET SOIL : VSM 40 %
DRY SOIL : VSM 5 %
HH-POL
Angle of Incidence : 45°
VV-POL
The effect of Incidence Angle
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HH-POL
VV-POL
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http://bte.bilgem.tubitak.gov.tr/tr/uygu-yo2014
[email protected]
Teşekkürler...
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G5-1-2 - bilişim teknolojileri enstitüsü

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