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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
Current Microwave Surface Scattering Models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Why do we need a simple surface Emission model?   ,[object Object],[object Object],[object Object],Microwave signals ,[object Object],[object Object],[object Object]
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  ,[object Object],[object Object],[object Object],[object Object]
Monte Carlo Simulation ,[object Object],[object Object],[object Object],[object Object],E v E h 40 ° 50 ° ,[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],AQUA Satellite ,[object Object],[object Object],[object Object],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%
[object Object],[object Object],[object Object],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 ,[object Object],[object Object],[object Object],[object Object],[object Object]

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IEM-2011-shi.ppt

  • 1. 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
  • 2.
  • 3.
  • 4.
  • 5. 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
  • 6.
  • 7.
  • 8. Validation of AIEM for Emission with Monte Carlo Model RMSE=0.01 RMSE=0.008 RMSE=0.017 RMSE=0.013
  • 9. 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 °
  • 10.
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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%
  • 16. Inverse algorithm Validation with INRA’93 Experimental Data at 50° RMSE=3.7% RMSE=3.5% RMSE=3.6% RMSE=3.5%
  • 17. Inverse algorithm Validation with USDA BARC (1979-1981) Experimental Data RMSE:2.9% RMSE:3.7% RMSE:3.6% RMSE:3.8%
  • 18.
  • 19. 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
  • 20. 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
  • 21. 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 %
  • 22.

Hinweis der Redaktion

  1. 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.
  2. 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
  3. 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