1. On Estimation of Surface Soil Moisture from SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara

2. Today’s Outline Image base algorithms for estimation of soil moisture Problems – roughness and vegetation Current available SARs – Single frequency and polarization –Concept and problem with current available SAR Multi-polarization SARs – Current available algorithms –Algorithm Development –On Improvement of bare surface inversion model On estimation of vegetated surface soil moisture with repeat- pass polarimetric measurements

3. Current Concept on Using Repeat-pass Measurements Basic Concept Two measurements => the relative change in dielectric properties The absolute dielectric properties <= one measurement is known

4. Tradition Backscattering Models

5. Problem of Repeat-pass Measurements Problems: Large dynamic range ks & kl => a different response of dielectric properties Roughness effects can not be eliminated Effect is greater VV than HH large incidence than small incidence Normalized Polarization functions - R/min(R) SP-VV SP-HH GO Relative moisture change in % 23°

6. Current Techniques Using Polarization Measurements Basic understanding on HH and VV difference: As dielectric constant, the difference As roughness (especially rms height), the difference As incidence angle, the difference Common idea of the current algorithms Inverse - two equations  two unknowns.

7. Current Algorithms for Bare Surface (1) Oh et al., 1992. Semi-empirical model  ground scatterometer measurements Using 3 polarizations  2 measurements

8. Current Algorithms for Bare Surface (2) Dubios et al., 1995 Semi-empirical model  ground scatterometer measurements Using 2 co-polarizations  2 measurements

9. Current Algorithms for Bare Surface (3) Shi et al., 1997. Semi-empirical model  IEM simulated most possible conditions Using 2 combined co-polarizations  2 measurements

10. Numerical Simulations by Multi- scattering IEM one 500 MHz alpha Workstation - more than 200 CPU hours for one incidence T3E supercomputer at GSFC/NASA - less than 3 CPU hours (160 processors)

11. Normalized Backscattering Coefficients HH+VV (HH*VV)^0.5 HH+VV (HH*VV)^0.5

12. Current Algorithms for Bare Surface (3) Shi et al., 1997. Semi-empirical model  IEM simulated most possible conditions Using 2 combined co-polarizations  2 measurements

13. Comparing Inverse Model with IEM

14. Sensitivity of Inverse Model to Calibration Absolute Error: ± Error in both HH & VV Relative Error: + Error in one & - error in the other 30°, 40°, 50°

15. Study Site Description

16. Experimental Description JPL L-band AIRSAR (June 10 – 18, 1992) VV, VH, HH 10 12 10 13 141618

17. Estimated Dielectric Constant Maps

18. Estimated Surface Roughness RMS Height Maps

19. Estimated Surface Roughness Correlation Length Maps

20. Estimated Soil Moisture Maps by SIR- C’s L-band Image in April, 1994

21. Estimated Surface RMS Height Maps by SIR-C’s L-band Image in April, 1994

22. Comparing Field Measurements Standard Error (RMSE)  3.4% in Soil Moisture estimation Standard Error (RMSE)  1.9 dB in roughness estimation

23. Basic Consideration (1) Common idea of the current algorithm Inverse - two equations  two unknowns. It can be re-ranged to one equation for one unknown. Disadvantages: Requires both formula all in good accuracy Error in the estimated one unknown  the other

24. Basic Consideration (1) - continue in (a) in (b) in (c) Different weight  sensitive to different surface parameter Independent direct estimation of soil moisture and RMS height (a) ks(b) Sr(c) Rh

25. Basic Consideration (2) IEM -- Power expansion and nonlinear relationships Higher order inverse formula  improve accuracy Example: estimate surface RMS height s s s’

26. Basic Consideration (3) Tradition Backscattering Models Inverse model for different roughness region  improve accuracy

27. Estimation of Surface RMS Height Inverse model Accuracy with the model simulated data Incidence in 0 RMSE in cm

28. Sensitivity Test on Estimation of RMS Height Absolute Error : to both VV and HH Relative Error : to one; and to the other Requires good calibration especially at small incidence absolute error in dB Incident angle model accuracy relative error =  0.5 dB absolute error =  2dB relative error in dB RMSE in cm 30 0 -0.3  n /2 0.3

29. Estimation of Dielectric Constant Two Hypothesis Test: 1) without separation of roughness regions 2) with separation of roughness regions 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Normalized average indicator = RhRh

30. Sensitivity Test on Estimation of Dielectric Constant Normalized average indictor The algorithm with separation of roughness region requires very accurate calibration Solid line with roughness separation Dotted line without roughness separation Solid line: model Dotted line: under absolute error  1 dB Dashed line: under relative 0.3 dB

31. Validation Using Michigan's Scatterometer Data  Correlation: m v - 0.75, rms height - 0.96  RMSE: m v - 4.1%, rms height - 0.34cm mvmv S RMSE for S Measured parameters Estimated incidence

32. Limitations of Using Polarization Measurements (A) - % of the simulated ratio > 1.0 dB (B) - % of the simulated  vh > -27 dB at C-band (C) - ratio in dB at L-band at 30° (D) - at 50°. Incidence angle % % C-Band L-Band C-Band C A B D 50° 30° Moisture in % Both with s=1.0 cm & cl=7.5 cm

33. Summary on Using Polarization Measurements Advantages of L-band VV and HH measurements  Larger dynamic range - directly estimate soil dielectric & RMS height  Less sensitive to vegetation effects Problems:  HH and VV has a little dynamic range at small incidence  Effect of the system noise on  vh measurements  HH and VV difference - saturation at high incidence & moisture C-band polarization measurements has much less advantages than L-band

34. Characteristics of Backscattering Model (4) First-order backscattering model Surface parameters – surface dielectric and roughness properties Vegetation parameters – dielectric properties, scatter number densities, shapes, size, size distribution, & orientation Fraction of vegetation cover Direct volume backscattering (1) Direct surface backscattering (4 & 3) Surface & volume interaction (2) Double pass extinction

35. Radar Target Decomposition Covariance (or correlation) matrix Decomposition based on eigenvalues and eigenvectors where, are the eigenvalues of the covariance matrix, k are the eigenvectors, and k’ means the adjoint (complex conjugate transposed ) of k.

36. Eigenvalues

37. Eigenvectors

38. Radar Target Decomposition Technique Total Power: single, double, multi Total Power: single, double, multi VV: single, double, multi VV: single, double, multi HH Correlation or covariance matrix -> Eigen values & vectors VV, HH, VH

39. Relationships in scattering components between decomposition and backscattering model 1.First component in decomposition (single scattering) – direct volume, surface & its passes vegetation 2.Second component (double-bounce scattering) – Surface & volume interaction terms 3.Third component – defuse or multi-scattering terms

40. Properties of Double Scattering Component in Time Series Measurements 1.In backscattering Model 2.Variation in Time Scale surface roughness vegetation growth surface soil moisture 3.Ratio of two measurements independent of vegetation properties depends only on the reflectivity ratio

41. Comparison with Field Measurements VV, HH, VH Two Corn Fields Dielectric Constant Date Normalized VV & HH cross product of double scattering components for any n < m Corresponding reflectivity ratio Correlation=0.93, RMSE=0.42 dB

42. Summary Time series measurements with second decomposed components (double reflection) provide a direct and simple technique to estimate soil moisture for vegetated surface Advantages of this technique is –Do not require any information on vegetation –Can be applied to partially covered vegetation surface

43. Discussion Current understanding Repeat-pass technique still requires surface roughness information. C-band is less sensitive to roughness than L-band. Polarization technique  L-band is better than C-band Repeat-pass + polarimetric technique  high potential on estimating vegetated surface soil moisture. L-band is better than C-band

44. Today’s Outline Image base algorithms for estimation of soil moisture Problems – roughness and vegetation Current available SARs – Single frequency and polarization –Concept and problem with current available SAR Multi-polarization SARs – Current available algorithms –Algorithm Development –On Improvement of bare surface inversion model On estimation of vegetated surface soil moisture with repeat- pass polarimetric measurements