kun-shan chen national central university, taiwan

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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

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APPLICATIONS OF THE INTEGRAL EQUATION MODEL IN MICROWAVE REMOTE SENSING OF LAND SURFACE PARAMETERS In Honor of Prof. Adrian K. Fung. Jiancheng Shi Institute of Remote Sensing Applications, CSA , Beijing, China & University of California, Santa Barbara. Kun-Shan Chen - PowerPoint PPT Presentation

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Page 1: 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

Page 2: Kun-Shan Chen National Central University,  Taiwan

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

Page 3: Kun-Shan Chen National Central University,  Taiwan

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

Page 4: Kun-Shan Chen National Central University,  Taiwan

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)

Page 5: Kun-Shan Chen National Central University,  Taiwan

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

Page 6: Kun-Shan Chen National Central University,  Taiwan

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

Page 7: Kun-Shan Chen National Central University,  Taiwan

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

Page 8: Kun-Shan Chen National Central University,  Taiwan

Validation of AIEM for Emission with Monte Carlo Model

RMSE=0.01

RMSE=0.008 RMSE=0.017

RMSE=0.013

Page 9: Kun-Shan Chen National Central University,  Taiwan

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°

Page 10: Kun-Shan Chen National Central University,  Taiwan

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

Page 11: Kun-Shan Chen National Central University,  Taiwan

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

Page 12: Kun-Shan Chen National Central University,  Taiwan

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

Page 13: Kun-Shan Chen National Central University,  Taiwan

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

Page 14: Kun-Shan Chen National Central University,  Taiwan

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

Page 15: Kun-Shan Chen National Central University,  Taiwan

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%

Page 16: Kun-Shan Chen National Central University,  Taiwan

Inverse algorithm Validation with INRA’93 Experimental Data at 50°

RMSE=3.7%

RMSE=3.5% RMSE=3.6%

RMSE=3.5%

Page 17: Kun-Shan Chen National Central University,  Taiwan

Inverse algorithm Validation with USDA BARC (1979-1981)

Experimental Data

RMSE:2.9%

RMSE:3.7%

RMSE:3.6%

RMSE:3.8%

Page 18: Kun-Shan Chen National Central University,  Taiwan

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

Page 19: Kun-Shan Chen National Central University,  Taiwan

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

Page 20: Kun-Shan Chen National Central University,  Taiwan

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

Page 21: Kun-Shan Chen National Central University,  Taiwan

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 %

Page 22: Kun-Shan Chen National Central University,  Taiwan

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)