Kun-Shan Chen National Central University, Taiwan
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Transcript of Kun-Shan Chen National Central University, Taiwan
APPLICATIONS OF THE INTEGRAL EQUATION MODEL IN MICROWAVE
REMOTE SENSING OF LAND SURFACE PARAMETERS
In Honor of Prof. Adrian K. Fung
Kun-Shan Chen
National Central University, Taiwan
Jiancheng Shi
Institute of Remote Sensing Applications, CSA , Beijing, China
& University of California, Santa Barbara
Current Microwave Surface Scattering Models
Importance of surface scattering modeling
• Direct component of soil moisture and ocean properties
• Boundary conditions for many other investigations of Earth geophysical properties (vegetation, snow, atmospheric properties)
Physical based surface scattering and emission models
– Tradition models
• Small Perturbation Model
• Physical Optical Model
• Geometrical Optical Model
– Integral Equation Model(s) (IEM, AIEM: analytical solution of above 3 models)
– Monte Carlo Model
Outline
1. Validation of IEM with 3D Monte Carlo simulated data and field measurements
2. Two examples for Multi-frequency AMSR-E and L-band SMOS and SMAP
• Soil surface parameterized model development;
• Inversion model development;
• Validation with ground radiometer measurements
Why do we need a simple surface Emission model?
1. Complex and computational intensive of AIEM - Image based analyses for global scale require a simple model
2. The simple model directly serves as the inversion model for soil moisture estimation
3. The simple model also serves as the boundary condition for other geophysical and atmospheric study
Microwave signals
4. Current available semi-empirical models
• Often derived from the limited experimental data . There are many uncertainties
• Most of available models fails to describe the characteristics of effects of surface roughness on emission signals at large incidence and high frequencies (AMSR-E, SSM/I, SSM/R, WINSAT, CIMS)
Numerical Simulations Using IEM&AIEM
Low up interval unit
Soil moisture 5.0 50.0 2.0 % by volume
RMS 0.25 3.5 0.25 cm
Correlation length 5.0 35.0 2.5 cm
Incidence angle 20.0 60.0 1.0 degree
Correlation function Gauss and Exponential
Development of the parameterized simple models and inversion algorithms from AIEM model simulated database for a wide range of soil dielectric and roughness conditions
Effects of Surface Roughness on Effective Reflectivities
• Common understanding:
surface roughness results in a decrease of the surface effective reflectivity or an increase of emissivity
• It was found:
surface roughness can result in a decreasing surface emissivity in V polarization <= both Monte Carlo and IEM models at high angle
Monte Carlo Simulation
At 50° - 257 cases• rms height: 0.035, 0.05, 0.1, 0.12, 0.15, 0.3, and 0.41 wavelength correlation length: 0.17 – 1.3 wavelength Dielectric constant: 3.6 – 24.6
vr1
Ev
Eh
vr1
hr1 hr1
40° 50°At 40° - 216 cases• rms height: 0.05, 0.1, and 0.15 wavelength correlation length: 0.33 – 1 wavelength Dielectric constant: 4.06 – 24.6
Both with Gauss function
Validation of AIEM for Emission with Monte Carlo Model
RMSE=0.01
RMSE=0.008 RMSE=0.017
RMSE=0.013
Validation of AIEM Model with Field Experimental Data
INRA’93 ground multi-frequency (5.05, 10.65, 23.8, and 36.5 GHz) and polarization (V & H) radiometer experimental data at 50°
Sensor Specifications
• Launched on May 4, 2002• Sun-synchronous orbit• Equatorial crossing at 13:30 LST (ascending)
AQUA Satellite
First Example for Soil Moisture Algorithm Development for AMSR-
E
• 12 channel, 6 frequency conically scanning passive microwave radiometer
• Earth incidence angle of 55°
• Built by the Japan Aerospace Exploration Agency (JAXA)
AMSR-E: Advanced Microwave Scanning Radiometer
Comparing Qp and AIEM Models
Frequency in GHz
6.925 10.65 18.7 23.8 36.5
0.0016 0.0012 0.0011 0.0011 0.0012
0.0023 0.0022 0.0017 0.0019 0.0016
V Polarization
H Polarization
ppqpep r)Q(rQR 1New Qp model
Qp is the polarization dependent roughness parameters
Surface Roughness Parameterization for Qp Model
lsclsbaQ pppp /)/log()log(
The surface roughness parameters Qp are highly correlated with the ratio of rms height –s and correlation length – l (proportion to random rough surface slope).
s/l s/l
Relationship in Roughness Parameters Qp
High correlation in roughness parameters can be found between Qh and Qv at different frequencies
Qh(f) = a (f)+ b(f)*Qv
Qv
Qh
6.925 GHz 10.65GHz 18.7 GHz 36.5 GHz
Est. Qv
Qv
Inverse algorithm for Bare Surface
hvsh
sv tctbEEa
After re-range, the algorithm:
)()()( vr mffleftf
Left side of Eq is from the measurements
Right side of Eq is only dependent on surface dielectric constant
pp rt 1
Therefore
Inverse algorithm Accuracies from AIEM Simulated Data
Input Mv in %
Estimated Mv in %
6.925 GHz
36.5 GHz18.7 GHz
10.65 GHz
RMSE=0.44%RMSE=0.30%
RMSE=0.28%RMSE=0.28%
Inverse algorithm Validation with INRA’93 Experimental Data at 50°
RMSE=3.7%
RMSE=3.5% RMSE=3.6%
RMSE=3.5%
Inverse algorithm Validation with USDA BARC (1979-1981)
Experimental Data
RMSE:2.9%
RMSE:3.7%
RMSE:3.6%
RMSE:3.8%
Current and Future satellite L-band radiometers: • SMOS – Multi-incidence, 50 km resolution, V and H
polarization• SMAP – Passive: 40 km, V and H polarizations, active:
1 – 3 km, VV, HH, and VH polarizations.
SMOS SMAP
Second Example: Applications for L-band Sensors
The Parameterized L-band Surface Emissivity Model
epR
The parameterized surface emissivity Model)()()(1)(1)( pB
ppep
sp rARE
sh
sh
E
E
1
2
sv
sv
E
E
1
2
VH
Absolute and ratio accuracies between IEM and the parameterized model
RM
SE
Viewing Angle
pr and are the effective and fresnel reflectivity. A and B are parameters depending on the roughness
High correlation in roughness parameters can be found
After re-range, the algorithm can be developed
Av
Av/Bv Ah/Bh
Ah Bh
Bv/Bh
)()(/ vrhv mffrr
Then
)()()())(log()())(log()()(exp)(
)( e
hev
eh
ev
h
v RRDRCRBAr
r
40°
L-band Inversion Model
Validation of Bare Surface Algorithm Using L-band Radiometer Measurements
(79-82) at USDA-BARC
20° 30° 40°
50° 60°RMSE
bias
RMSE=2.9 %
RMSE=3.1 %
RMSE=2.8 %RMSE=2.6 %
RMSE=3.6 %
Summary on IEM/AIEM Contributions
Providing an important tool for algorithm(s) development in Earth surface geophysical properties retrieval
Other application examples:
1.Soil Moisture retrieval for L-band radar (SMAP and POLSAR, Sun et al., IGARSS 2010)
2.Retrieval vegetation properties for AMRS-E (Shi et al., RSE, 112(12) 4285-4300, 2008) and for SMOS (Chen et al., IEEE/GRSL 7(1):127-130, 2010)
3.Snow parameterized model(s) for AMSR-E (Jiang et al., RSE, 111 (2-3) 357-366, Nov. 2007 and CoreH2O (Du et al., RSE, 114 ( 5 ): 1089-1098 , 2010)