Estimation of surface soil moisture at the intra-plot spatial scale by using low and high incidence angles TerraSAR-X images

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).