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
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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
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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 °
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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%
17. Inverse algorithm Validation with USDA BARC (1979-1981) Experimental Data RMSE:2.9% RMSE:3.7% RMSE:3.6% RMSE:3.8%
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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 %
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Editor's Notes
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
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