When the soil moisture is high, the total backscattering will be due primarily to the surface scattering because the reflectivity at the surface is significantly more and less power is transmitted across the air-soil interface. However, the volume scattering can not be neglected under nearly perfectly dry conditions. Figure 1 compares two cases of calculating the backscattering coefficients (s ⁰_hh and s⁰_vv), one considering only surface scattering (the polarizations of HH and VV are represented by the symbols x and +) and the other considering both surface and volume scattering (solid and dotted curves), as a function of Mv at an incidence angle (q_s) of 40 degree, an rms surface height (s) of 1.4cm, =l 15cm, and the exponential correlation function are assumed. If the soil moisture content is under 2%, the differences between the two cases of calculating backscatter for both polarizations exceed 1 dB. Figures 2 to 4 show results of the sensitivity analysis for all polarization states of the model evaluated for both surface and volume scattering at 1.27 GHz (L-band), q_s= 40 degree, and an assumed exponential correlation function as a function of Mv (Fig. 2),s (Fig. 3), and l (Fig. 4). It will help to select polarization measurements that are most sensitive to each parameter. The backscattering coefficients (in dB) are plotted for s ⁰_hh (solid curve), s ⁰_vv (dotted curve), s⁰_hh+s ⁰_vv (x), (+), and s ⁰_hh/s ⁰_vv (*). Figure 2 clearly shows that the dependence of s⁰_hh and s⁰_vv on Mv is nonlinear. For example, s⁰_hh increases about 8.2dB as Mv varies from 2 to 50% as compared to 12.1dB for s ⁰_vv , 10.9dB for s⁰_hh + s ⁰_vv, 10.1dB for , and 3.9 dB for s⁰_hh/s⁰_vv . Also, s⁰_hh reaches saturation faster than the other polarization states. To simulate dependence of backscattering on s , Fig. 3 assumes wet soil ( Mv= 30%) and l = 15cm. In the figure, s⁰_hh increases about 16.8dB as s varies from 0.2 to 3.6cm as compared to 16.4dB for s⁰_vv , 16.5dB for s⁰_hh+s⁰_vv and , and 2.1 dB for s⁰_hh/s⁰_vv. Furthermore, the curve shapes are similar to those in Fig. 2 for each polarization state. From Fig. 4 (s = 1.4cm; Mv= 30%), the sensitive of l is less to backscatter than other parameters (i.e., Mv and s ).s⁰_hh/s⁰_vv increases about 1 dB from 2.5 to 20 cm as compared to about 3 dB for others.
Figure 1. Effect of volume scattering on soil moisture variation at 1.27GHz, 40 degree incidence angle. An exponential surface correlation with s=1.4cm and l =15cm was used. Symbols x and + represent surface scattering, the solid and dotted lines represent surface and volume scattering.

Figure 2. Numerical simulation of backscattering dependence on soil moisture by polarization states. The solid, dotted, x, + , and * are for s ⁰_hh,s ⁰_vv, s ⁰_hh+s ⁰_vv,,s ⁰_hh/s ⁰_vv, respectively. Input parameters were the same as in Fig. 1.


Figure 3. Simulated backscattering dependence on rms surface height at the Mv =30%. Other parameters were the same as in Fig. 1.



Figure 4. Simulated backscattering dependence on correlation length at s =1.4cm. Other parameters were the same as in Fig. 3.

Comparison With Field Experiment

Simultaneous Experiment
A simultaneous experiment with PI-SAR was conducted on July 14, 1999. However, the selected day was not good for validating the surface soil moisture because it rained very much. Three test sites with bare land or short vegetation were selected to validate the soil moisture and roughness. Two test sites were located in the “Tomakomai Experiment Forest” of Hokkaido University, Japan (see Qong et al., 1999), which is a very flat forest area and very well maintained. The surface soil moisture was obtained by the Time-Domain Reflectometry (TDR) method and direct sampling method; the horizontal profiles of the land surface height were measured by comb-style instruments (80cm length, 2mm interval) to evaluate the surface roughness parameters (i.e., s , l , and surface correlation function). The ground truth data of one test site in the felling forest area was used in this study.