Identifying Time Patterns at the Field Scale for Retrieving Superficial Soil Moisture on an Agricultural Area with a Change Detection Method: A Preliminary Analysis

A preliminary analysis based on the application of a change detection method for remote sensed soil moisture retrieval at high resolution is presented. Sentinel-1 SAR images are used for studying agricultural areas in Spain, here in situ soil moisture data are available through the International Soil Moisture Network. The total backscattered SAR signal is modelled as the sum of vegetation and soil contributions. At first, the relationship between soil moisture and the co-polarized band of Sentinel-1 was analyzed for all the measurement stations of the area, and the ones with stronger relation were selected. Time series analyses were then conducted at the field scale for studying the interactions between some SAR parameters and the in situ data. The two polarizations and the polarization ratio were analyzed with respect to in situ soil moisture observations and precipitation data in order to identify homogeneous time domains in which the method can be applied in a consistent manner. Analyses show that the main driver of wide range SAR signal variations is the presence of precipitation events. Moreover, SAR coherence and polarization rate manifest specific behaviors that can be exploited either for deepening the knowledge on the role of model parameters and identifying suitable time and space extends in which operate separate estimations of vegetation, soil moisture and soil roughness parameters. Identification and isolation of precipitation driven patterns, as long as the selection of homogeneous time spans and space regions is the basis for improving the capability of satellite based soil moisture retrieval models.