Evaluation of Surface Roughness Parameters and Correlation Function
A total number of 18 surface roughness profiles were obtained in one test site (two profiles for each measured point). s and l were calculated from these data (Ulaby et al., 1986), and the results are shown in Fig. 5. To find the surface correlation function, eq. (1) was fitted to the measured autocorrelation from the measured roughness profiles for various values of n . Figure 6 shows an example of the measured auto correlation compared with the exponential and Gaussian correlation functions; the results are summarized in Fig. 7 where the number of occurrences was plotted as a function of the power n (Shi et al., 1997). Figure 7 indicates that about 67% of the measured roughness profiles at this test site can be described by exponential correlation functions with n£1.4.

Comparison of Measured and Calculated Backscattering Coefficients
Figure 8 compares s⁰_hh and s⁰_vv obtained by PI-SAR and calculated by the model with measured Mv, s , l , and 1 =n input for the test site. Besides roughness measurements, Mv was sampled three times by TDR at one point, then calibrated and averaged for each point. Figure 8 indicates that some of the calculated backscattering coefficients were in good agreement with measured ones. However, calculated s⁰_hh values were almost always underestimated.



Figure 5. Relationship between measured s and l at the test site.



Figure 6. Example of comparisons between measured autocorrelation (solid curve) and general correlation with n=1.0, 1.4, 2.0 (dotted curves).



Figure 7. Summary of the result of fitting the surface correlation function to measured autocorrelation.



Figure 8. Comparisons between backscattering coefficients observed by PI-SAR and calculated using measured parameters, Mv , s and l . The data points for s⁰_hh and s⁰_vv are represented by the symbols and +, respectively.

Simple Algorithm for Estimating Moisture and Roughness
A simple algorithm is used to estimate the soil moisture and surface rms height simultaneously from polarimetric SAR data, employing only s⁰_hh and s⁰_vv and assuming l . Figure 9 shows an example of the inversion diagram at 1.27GHz, 38.9 degree incidence angle corresponding to the test site, l = 15 cm, and exponential correlation function. The variations of Mv and sare represented by the solid and dotted curves, respectively, and the measured values, by x. If the measured value is plotted in the simulated range, Mv and s for each pixel can be inferred from Fig. 9. However, it is difficult to consider all pixels, and only five pixels can be used in this case. Figure 10 summarizes the inferred results for Mv. Figure 10 indicates that the soil moisture is underestimated. Also, the inferred s is overestimated, which is the same as the trends of Hajnsek et al. (1999).

Figure 9. Inversion diagram using s⁰_hh and s⁰_vv at l=15cm. The solid curves show variations of Mv; the dotted curves, the variation of s; and the symbol x, observed by PI-SAR at the test site.



Figure 10. Comparisons between the inferred and ground measured soil moisture for the test site. Only five pixels at the test site could be calculated.

Conclusions
This study sought to infer the surface soil moisture and roughness based on a sensitivity analysis performed using a scattering model including the surface correlation function, then to compare the results with data from a simultaneous PI-SAR experiment. Because the problem is site- specific, the measurement accuracy of both the ground truth data and the SAR system, including speckle noise, is not optimum, and the model has uncertainty, measured and inferred values of the soil moisture and surface roughness did not agree well. It is thus necessary to examine the algorithms to estimate the soil moisture based on the radiative transfer solutions.

Acknowledgements
The authors would like to thank Prof. Hiura and Mr. Okuyama of Hokkaido University for implementing the field experiment, and their CRL colleagues and Mr. Kawada of RESTEC for processing the PI-SAR data.

References

• Dobson, M. C. et al., 1985. Microwave dielectric behavior of wet soil, Part II: Dielectric mixing models. IEEE Trans. Geosci. Remote Sensing, GE-23, pp.35-46.
• Dubois, P. C. et al., 1995. Measuring soil moisture with imaging radar. IEEE Trans. Geosci. Remote Sensing, 33, pp.915-926.
• Engman, E. T., and N. Chauhan, 1995. Status of microwave soil moisture measurements with remote sensing. Remote Sensing Environ., 51 (1), pp.189-198.
• Fung, A. K., 1994. Microwave Scattering and Emission Models and Their Applications, Norwood, MA: Artech House.
• Hajnsek, I. et al., 1999. Determination of hydrological parameters using Airborne-Radar data (DLR E-SAR). IEEE IGARSS ’99 Proceedings, pp.1108-1110.
• Oh, Y. et al., 1992. An empirical model and inversion technique for radar scattering from bare soil surface. IEEE Trans. Geosci. Remote Sensing, 30, pp.370-378.
• Qong, M. et al., 1999. Land Cover Classification Using CRL/NASDA PI-SAR Data, The 20 th Asian Conference on Remote Sensing proceedings (being submitted).
• Shi, J., and J. Dozier, 1995. Inferring snow wetness using C-band data from SIR-C’s polarimetric Synthetic Aperture Radar. IEEE Trans. Geosci. Remote Sensing, 33, pp.905-914.
• Shi, J. et al., 1997. Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Trans. Geosci. Remote Sensing, 35, pp.1254-1265.
• Tadono, T. et al., 1999. Development of a SAR algorithm for soil moisture mapping in permafrost regions including the effect of surface roughness. JSCE Jornal of Hydroscience and Hydraulic Engineering (being submitted).
• Ulaby, F. T. et al., 1986. Microwave Remote Sensing, Active and Passive, MA: Artech House. Wakabayashi, H. et al., 1999. Airborne L-band SAR system: Characteristics and initial calibration results. IEEE IGARSS ’99 Proceedings, pp.464-466.
• Wang, J. R. et al., 1986. The SIR-B observations of microwave dependence on soil moisture, surface roughness, and vegetation covers. IEEE Trans. Geosci. Remote Sensing, 24, pp.510-516.