SAR for superficial soil moisture retrieval at the field scale over an agricultural area

Not many studies are currently devoted to the estimation of soil moisture from space-borne SAR data at the field scale. Superficial soil moisture is indeed generally estimated from SAR images at lower resolutions, rarely reaching the sub-kilometric scale. This is mainly due to the lack of in situ data, such as measured soil moisture and parameters indicative of the soil roughness and vegetation conditions. Moreover, when working at the kilometric scale, some hypothesis assumed while modelling the backscattered SAR signal over a vegetated area are more likely satisfied, whereas when working at higher resolutions such as the field scale, other interactions should be taken into account. Indeed, over a vegetated area the total backscattered SAR signal is usually modelled as the incoherent sum of the vegetation and the soil components, and only in the last years has been added a further contribution provoked from the presence of subsurface scatterers. In the present thesis, the just mentioned contributions are considered and modelled at the field scale for soil moisture estimation purposes. A long term Change Detection method is applied to copolarized Sentinel-1 data, with a focus on taking into account the component of the total backscattering coefficient due to the presence of subsurface scatterers, recently proposed in literature. By exploiting the strong relationships detected over the study area between the copolarized signal and the observed soil moisture, the inversion algorithm for soil moisture retrieval is adapted for considering the cases of dominant subsurface scattering mechanism. Moreover, the proper time scale of detection of subsurface scattering is identified at the field scale, providing helpful information for correcting retrieval algorithms based on SAR data also at lower spatial scales.