How important is the change-of-support problem when digital soil mapping involves multi-source spatial data fusion? A real-world application of multivariate geostatistics to the regional scale of Campania (Italy).

Effective techniques for spatial data analysis are required to address the growing need to develop and improve management plans at regional or continental scales. The main purpose of this research is to evaluate the impact of change of spatial support on digital soil mapping. The study is based on the availability of more than 3,300 soil samples collected from the uppermost horizons of farmlands in Campania, an administrative region of southern Italy of about 13,700 km². Each soil sample was subjected to laboratory tests to determine the following point-referenced primary soil properties: sand and clay contents, oven-dry soil bulk density, soil organic matter, pH, calcium carbonate, and rock fragments. These seven soil properties were mapped over the entire region by using as covariates the following terrain attributes, obtained from the digital terrain model (DTM; 75 m/pixel): elevation, slope, plan curvature, profile curvature, and flow accumulation. Two composite indicators of soil quality were then determined: 1) the soil organic carbon stock (SOCS) in Campania, and 2) the recharge transit time in the alluvial plain of the Sele River where information about the mean annual depth to groundwater is available.

In this study, it was crucial to evaluate the epistemic uncertainty associated with the change of support when fusing the point-referenced soil measurements with the block-based terrain attributes. A key issue of our analysis is the modification of the anamorphosis model based on a block rather than a point. Accordingly, our results show how the estimates change when a properly-corrected block Gaussian anamorphosis model is employed instead of the point Gaussian one.

By way of conclusion, when the main interest of an investigation is to obtain a map of average soil attributes, the change of support might have little influence on the final estimates, especially when working with nearly symmetrical distributions of soil properties. On the other hand, if one should infer the uncertainty of a variable, as in soil vulnerability mapping, then the change of support matters and is an issue to be adequately accounted for in the spatial analysis of environmental data.