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IEM-2011-shi.ppt
 

IEM-2011-shi.ppt

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  • For H polarization, roughness increases emissivity. However, roughness decreases emissivity at large angle for V polarization. At 50 degree, roughness will enhance the emissivity difference. Effects of roughness are different at different polarization.
  • There are many forms that can be formulated for semi-empirical model. Consideration focus on both ratio measurements and the absolute value. The formula above are based on following evaluation: The effective reflectivity ratio on first row indicates that roughness correction is needed for V but not for H The emissivity ratio on second row indicates that that roughness correction is needed for H but not for V
  • There are many forms that can be formulated for semi-empirical model. Consideration focus on both ratio measurements and the absolute value. The formula above are based on following evaluation: The effective reflectivity ratio on first row indicates that roughness correction is needed for V but not for H The emissivity ratio on second row indicates that that roughness correction is needed for H but not for V

IEM-2011-shi.ppt IEM-2011-shi.ppt Presentation Transcript

  • 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
    • Validation of IEM with 3D Monte Carlo simulated data and field measurements
    • 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?
    • Complex and computational intensive of AIEM - Image based analyses for global scale require a simple model
    • The simple model directly serves as the inversion model for soil moisture estimation
    • 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 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
    E v E h 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 °
  • First Example for Soil Moisture Algorithm Development for AMSR-E Sensor Specifications
    • Launched on May 4, 2002
    • Sun-synchronous orbit
    • Equatorial crossing at 13:30 LST (ascending)
    AQUA Satellite
    • 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 New Qp model Qp is the polarization dependent roughness parameters
  • Surface Roughness Parameterization for Qp Model 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 Q h (f) = a (f)+ b(f)*Q v Q v Q h 6.925 GHz 10.65GHz 18.7 GHz 36.5 GHz Est. Q v Q v
  • Inverse algorithm for Bare Surface After re-range, the algorithm: Left side of Eq is from the measurements Right side of Eq is only dependent on surface dielectric constant Therefore
  • Inverse algorithm Accuracies from AIEM Simulated Data Input Mv in % Estimated Mv in % 6.925 GHz 36.5 GHz 18.7 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 The parameterized surface emissivity Model V H Absolute and ratio accuracies between IEM and the parameterized model RMSE Viewing Angle 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 A v A v / B v A h / B h A h B h B v / B h Then 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:
    • Soil Moisture retrieval for L-band radar ( SMAP and POLSAR, Sun et al., IGARSS 2010 )
    • 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 )
    • 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 )