Retrieving Soil Moisture at the Field Scale from Sentinel-1 Data over a Semi-Arid Mediterranean Agricultural Area

In this work, superficial soil moisture is estimated from SAR data at the field scale on agricultural fields over which the relationship between the co-polarized backscattering coefficient (?(VV)(0)) and the measured soil moisture (SSMv) is both direct and inverse. An inversion algorithm is adapted to the charateristics of the single field and applied to SAR signal differences. The differences of SAR signal are obtained from a change detection (CD) method applied on the VV band of the Sentinel-1 SAR mission. In the CD method, the variations of the total backscattered signal due to sharp changes in vegetation and soil roughness are excluded from the dataset by using a machine learning algorithm. The retrieval method is applied on a low vegetated agricultural area in Spain, characterized by a semi-arid mediterranean climate and where in situ soil moisture data are available. Good results are obtained not only over fields characterized by direct ?0VV/SSMv relationship, reaching values of correlation coefficient and RMSE up to r=0.89 and RMSE=0.042 m(3)/m(3), but also over fields with inverse relationship, obtaining in this case values up to r=0.84 ad RMSE=0.026 m(3)/m(3). Although the inverse relationship between the backscattering coefficient and the measured soil moisture is not yet well understood in the field of soil moisture estimation from radar data, for the present case, checking the nature of this relationship was fundamental in order to accordingly adapt the soil moisture retrieval algorithm to the dataset characteristics.