Interpolation selection index for delineation of thematic maps

Abstract

Precision agriculture aims at the precise management of agricultural inputs toward increasing profits, decreasing losses, and preserving the environment. Thus, the use of thematic maps to understand the behavior of involved attributes is important, and the construction of these maps usually involves some type of interpolation. Choosing the best interpolation method can be difficult, and the use of cross-validation is not trivial. Therefore, the purpose of this study is to propose a selection index of interpolation methods that will help in choosing the best deterministic and stochastic model among those evaluated. The study was conducted within a 15.5-ha area in Southern Brazil, and an interpolation selection index (ISI) was applied to data on clay, copper, and manganese content, and apparent electrical conductivity of the soil using four interpolation methods: inverse distance, inverse distance squared, ordinary kriging, and cokriging. Using the ISI, choosing between deterministic and stochastic interpolation methods is simplified and less subjective. In cases where the deterministic interpolator (inverse of distance squared) was chosen, the spatial dependency was moderated. Note that the proposed statistic (ISI) does not quantify the difference between the analyzed methods.