Several studies have demonstrated the usefulness of SAR satellite images for monitoring surface parameters, in particular regular estimates of surface soil moisture (SSM) at regional (i.e., swath of several km²) or plot (i.e., area of interest of several hectares) spatial scales. These scales are rarely suitable for precision farming which requires mapping SSM at intra-plot spatial scale. In this context, the objective of this study is to analyze the capabilities of multi-temporal TerraSAR-X images to estimate the fine-scale SSM variability over bare agricultural plots (at a spatial scale ranging from 80 to 2800 m²). Time series of X-band satellite images were collected over a study site located in southwestern France, together with intra-field measurements of key soil descriptors (i.e., SSM, surface roughness, soil texture). The large dataset allows independent training and validating steps of a statistical algorithm (random forest), SSM being estimated using images acquired at low at high incidence angles. The level of performances obtained at the plot spatial scale, with R² ranging from 0.64 to 0.67 (depending on the considered incidence angle) and a RMSE close to 5.0 m^-3.m^-3, are exceeded by those obtained at a finer scale (700 m², corresponding to buffers with a 15 m radius). At this intra-plot spatial scale, the estimates based on the low incidence angles images are associated to a R² of 0.69 and a RMSE of 4.89 m^-3.m^-3, results slightly lower than performance obtained using high incidence angles images, R² of 0.72 and a RMSE of 4.55 m^-3.m^-3. Such magnitude of performance slightly increases over larger intra-plot spatial scales, the values of R² being superior to 0.75 with RMSE lower than 4.20 m^-3.m^-3 over areas of 2800 m² (corresponding to buffers with a 30 m radius).