inversion of surface parameters from nasa/jpl airsar...
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Inversion of Surface Parameters from NASA/JPL AIRSAR Polarimetric SAR Data
Sang-Eun Park1, Jun-Su Kim1, Wooil M. Moon1,2, Wolfgang-Martin Boerner3
1. School of Earth and Environmental Sciences, Seoul National University.Phone/Fax: +82-2-880-8898 / +82-2-871-3269Email: [email protected]; [email protected]; [email protected]
2. Geophysics, University of Manitoba.Phone/Fax: +1-(204)-474-9833 / +1-(204)-474-7623 Email: [email protected]
3. Communications, Sensing & Navigation Laboratory, Department of Electrical & Computer Engineering, University of Illinois at Chicago.Phone&Fax: +1-(312)-996-5480Email: [email protected], [email protected]
POLinSAR 2007
ESI3 Lab., SNU-2-
POLinSAR 2007
Models for Scattering from a Rough SurfaceModels for Scattering from a Rough Surface
Empirical regression models
Ulaby et al., 1982; Quesney et al., 2000; Zribi et al., 2002; Glenn and Carr, 2003
Coefficients describing the linear relationship are often different from one place to another and also from one year to the next.
Small Perturbation Method (SPM), Geometric Optics Model (GOM), Physical Optics Model (POM)
Necessity for broadening the range of validity
Theoretical scattering models for random rough surface
Semi-empirical models [Oh et al., 1992; Oh, 2004][Dubois, 1995]
Extended Bragg Model [Schuler et al., 2002; Hajnsek et al., 2003]
ESI3 Lab., SNU-3-
POLinSAR 2007
Semi-Empirical ModelSemi-Empirical Model
• Uses the volumetric soil moisture content mV as an input parameter of the model
• Empirically determined function for the co- and cross-polarized backscatter ratios:
( ) ( )( )[ ]8.1
2.27.00
)(32.0exp1
cos11.0
ks
mVHV
−−
= θσ
( )4.1
35.0
0
0
)(4.0exp
901
65.0
ks
Vm
VV
HH
−⋅
⎟⎠⎞
⎜⎝⎛−=
−
o
θσσ
( ) )(9.0exp1
3.1sin1.0
8.0
2.1
0
0
ks
ls
VV
HV
−−
⎟⎠⎞
⎜⎝⎛ += θ
σσ
[Oh, 2004]
ESI3 Lab., SNU-4-
POLinSAR 2007
• Polarization Coherence
Extended-Bragg ModelExtended-Bragg Model***qqqqppppqqppppqq SSSSSS=ρ
Sensitive to “small scale roughness” while relatively insensitive to soil moisture.[Borgeaud and Noll, 1994; Mattia et al, 1997]
Need theoretical expression of the relationship between and surface parametersppqqρ
Small Perturbation Model (Bragg Scattering Model)
( )0,sin2)cos(8 42 θθ kWksms ⋅=
1||||
))((22
***
=>><<
><=
PS
PSPSHHVV
BB
BBBBρ
21 || Ps BBC += ∗−+= ))((2 PSPS BBBBC 2
21
3 || Ps BBC −=
Extended-Bragg Model: Consider the effect of azimuthal tilt [Cloude and Papathanassiou, 1999]
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡=
000020
2][ 3
*2
21
CCCC
mT s
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−=
ψψψψ
ψψψψψ
2cos2sin02sin2cos0
001 ][
2cos2sin02sin2cos0
001)]([ TT
Unable to describe polarization coherence
Averaged coherency matrix over the orientation distribution ∫= ψψψ
ψdpTT )()]([][
ESI3 Lab., SNU-5-
POLinSAR 2007
Extended-Bragg ModelExtended-Bragg Model
Assume Gaussian distribution of orientation angle induced by slope variation
[ ][ ]
)8exp(100
0)8exp(1)2exp(0)2exp(
2][
23
23
2*2
221
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−−−+−
−=
σσσ
σ
CCCCC
mT s
Standard deviation of the orientation angle distribution
Theoretical relationship between σ and the surface roughness parameters
sin
slope RMSθ
σ =1.5-power correlation function
, sin
s3θ
σl
=⎭⎬⎫
⎩⎨⎧
⎟⎟⎠
⎞⎜⎜⎝
⎛−=
θρ 22
2
sin24expls
RRLL
[Schuler et al., 2002; Hajnsek et al., 2003; Allain, 2005]
ESI3 Lab., SNU-6-
POLinSAR 2007
Estimation of Soil Moisture Contents in Jeju IslandEstimation of Soil Moisture Contents in Jeju Island
251-line 341-line
ESI3 Lab., SNU-7-
POLinSAR 2007
Volumetric Moisture Contents (In-situ)
Study AreaStudy Area
0.62 0.16 2.39 0.60 251-line
1.73 0.25 6.66 0.97 341-lineS2
0.42 0.10 1.64 0.40 251-line
0.76 0.20 2.93 0.77 341-lineS1
klksl (cm)s (cm)Direction (range)
Roughness parameters (In-situ)
ESI3 Lab., SNU-8-
POLinSAR 2007
Soil Moisture Estimation Soil Moisture Estimation
( ) ( ) ( )[ ]8.12.27.00 )(32.0exp1cos11.0 ksmVHV −−= θσ
( )4.135.0
0
0
)(4.0exp90
165.0
ksVm
VV
HH −⋅⎟⎠⎞
⎜⎝⎛−=
−
o
θσσ
( ) )(9.0exp1 3.1sin1.0 8.02.1
0
0
ksls
VV
HV −−⎟⎠⎞
⎜⎝⎛ += θ
σσ
( ) Ω→ΘFNonlinear Forward Mapping
( )Ω=Θ −1F
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
=Ω00
00
0
VVHV
VVHH
HV
σσσσ
σ
Ω =
Finding the set of unknown surface parameters from polarization measurement
Independent combination of polarization measurements
,, lsmV=Θ
Inversion of the semi-empirical model Inversion of the extended-Bragg model
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡=Ω
???
( ) ( ) ( ) [ ]
( ) [ ]
100010
2][
2
22
2
sin/243
sin/243
sin/6*2
sin/621
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−+=
−
−−
−
θ
θθ
θ
ls
lsls
ls
s
eCeCeCeCC
mT
ESI3 Lab., SNU-9-
POLinSAR 2007
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡ +
=Ω 22
22
2
VVHV
VVHH
vvHH
SSSSSS
Finding ΩFinding Ω
2 cm35 cm3 cml
0.2 cm3.4 cm0.2 cms
5355εr
IntervalMax.Min.Soil parameters
ESI3 Lab., SNU-10-
POLinSAR 2007
Semi-Empirical ModelSemi-Empirical Model⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
=Ω00
00
0
VVHV
VVHH
HV
σσσσ
σ
ESI3 Lab., SNU-11-
POLinSAR 2007
Extended-Bragg ModelExtended-Bragg Model⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡ +
=Ω 22
22
2
VVHV
VVHH
vvHH
SSSSSS
][][][][ CGCG CCCC ++=
(Radiative Transfer Theory) [Ulaby et al. 1992]
Incoherent Modeling for Vegetation Scattering
Soil Moisture Estimation for grasslandSoil Moisture Estimation for grassland
ESI3 Lab., SNU
Changes in Polarization ParametersChanges in Polarization Parameters⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡ +
=Ω 22
22
2
VVHV
VVHH
vvHH
SSSSSS
ESI3 Lab., SNUPOLinSAR 2007
ESI3 Lab., SNU-14-
POLinSAR 2007
Target Decomposition MethodsTarget Decomposition Methods
Incoherent Polarimetric Descriptors, [C] and [T](eigenvalue-eigenvector decomposition)
[ ] *333
*222
*111 kkkkkk λλλ ++=C [ ] *
333*222
*111 eeeeee λλλ ++=T
Parameterization of the Eigenvector of [T] Scattering mechanism of each scattering
contribution
[ ]1C [ ]2C [ ]3C
Three Scattering Contributions
[van Zyl, 1994]
[Cloude and Pottier, 1996]
[Wang and Davis, 1998]
[ ]1T [ ]2T [ ]3T
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡=
i
i
jii
jii
i
i
ee
γ
δ
βαβα
α
sinsincossin
cose
Finding Ω for GrasslandFinding Ω for Grassland( )( ) ⎥
⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−+
+=Ω *
1111
211
1
VVHHVVHH
VVHH
SSSSSS
α
ESI3 Lab., SNUPOLinSAR 2007
ESI3 Lab., SNU-16-
POLinSAR 2007
Effect of Orientation AngleEffect of Orientation Angle
)2,1(1T
)1,1(1T
1α
22VVHV SS
22VVHH SS
2vvHH SS +
22VVHV SS
22VVHH SS
2HVS
ESI3 Lab., SNU-17-
POLinSAR 2007
Inversion ResultsInversion Results
ESI3 Lab., SNU-18-
POLinSAR 2007
ConclusionsConclusions
In order to improve the operational applicability of polarimetric SAR remote sensing techniques for retrieving the spatial distribution and temporal variation of the surface geophysical parameters, inversion techniques of the polarimetric surface scattering models were presented.
The confounding influence of roughness on the estimation of the soil moisture contents were considered in the inversion algorithm that estimates volumetric moisture contents and roughness parameters simultaneously from the pertinent combination of polarization measurements.
An extension of the soil moisture inversion algorithm to a wider range of terrain types was presented by using the eigenvector based decomposition of the polarimetric SAR data.
The soil moisture content in vegetated areas can be obtained successfully from the eigen-parameters together with elements in the first eigen-contribution of the coherency matrix.
Improve or develop the methodology required to extract geo- and bio-physical parameters from POLSAR data
