Soil moisture is important information for agricultural fields in which erosion of upper soil layers depends upon the soil moisture and in which the yield depends on soil water contents during sowing, growing, and harvest periods. Although many studies have estimated moisture in bare soil fields using synthetic aperture radar (SAR) imaging, few models are useful to estimate volumetric soil moisture for wide areas because of requests for detailed roughness data. This study is intended to estimate soil moisture in bare soil fields using ALOS/PALSAR imaging. In the active microwave domain, the measured signal (backscattering coefficient) over bare soil depends on the soil moisture and surface roughness. Reducing surface roughness effects on backscattering coefficients is the main challenge confronting soil moisture estimation using single-frequency, single-polarization SAR data. First, we evaluated the relationships between the backscattering coefficient and RMS (Root Mean Square) height, an index of roughness, which is calculated with elevation data at 1.4-m intervals. Results showed that backscattering coefficients were correlated with RMS height. Consequently, backscattering in conditions with no volumetric soil moisture variation in the study area was simulated. The effect of roughness was reduced by subtracting the backscattering coefficients simulated using our approach from those of all obtained PALSAR images. Furthermore, we developed a linear approach using the regression line between the backscattering coefficient, which reduced the effects of roughness (Δσ ^o ), and measured volumetric soil moisture values. The approach developed in this study estimates soil moisture with 4.4% RMSE for validation data that were not used to produce the model.