Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors.