Delineation of Management Zones in Precision Agriculture by Integration of Proximal Sensing with Multivariate Geostatistics. Examples of Sensor Data Fusion

Fundamental to the philosophy of Precision Agriculture (PA) is the concept of matching inputs to needs. Recent research in PA has focused on use of Management Zones (MZ) that are field areas characterised by homogeneous attributes in landscape and soil conditions. Proximal sensing (such as Electromagnetic Induction (EMI), Ground Penetrating Radar (GPR) and X-ray fluorescence) can complement direct sampling and a multisensory platform can enable us to map soil features unambiguously. Several methods of multi-sensor data analysis have been developed to determine the location of subfield areas. Modern geostatistical techniques, treating variables as continua in a joint attribute and geographic space, offer the potential to analyse such data effectively. The objective of the paper is to show the potential of multivariate geostatistics to create MZ in the perspective of PA by integrating field data from different types of sensors, describing two study cases. In particular, in the first case study, cokriging and factorial cokriging were employed to produce thematic maps of soil trace elements and to delineate homogenous zones, respectively. In the second case, a multivariate geostatistical data-fusion technique (multi collocated cokriging) was applied to different geophysical sensor data (GPR and EMI), for stationary estimation of soil water content and for delineating within-field zone with different wetting degree. The results have shown that linking sensors of different type improves the overall assessment of soil and sensor data fusion could be effectively applied to delineate MZs in Precision Agriculture. However, techniques of data integration are urgently required as a result of the proliferation of data from different sources