Spatial interpolation methods to predic...

Spatial predictions of drift deposits on soil surface were conducted using eight different spatial interpolation methods i.e. classical approaches like the Thiessen method and kriging, and some advanced methods like spatial vine copulas, Karhunen-Loève expansion and INLA. In order to investigate the impact of the number of locations on the prediction, all spatial predictions were conducted using a set of 39 and 47 locations respectively. The analysis revealed that taking more locations into account increases the accuracy of the prediction and the extreme behavior of the data is better modeled. Leave-one-out cross-validation was used to assess the accuracy of the prediction. The Thiessen method has the highest prediction errors among all tested methods. Linear interpolation methods were able to better reproduce the extreme behavior at the first meters from the sprayed border and exhibited lower prediction errors as the number of data points increased. Especially the spatial copula method exhibited an obvious increase in prediction accuracy. The Karhunen-Loève expansion provided similar results as universal kriging and IDW, although showing a stronger change in the prediction as the number of locations increased. INLA predicted the pesticide dispersion to be smooth over the whole study area. Using Delaunay triangulation of the study area, the total pesticide concentration was estimated to be between 2.06% and 2.97% of the total Uranine applied. This work is a first attempt to completely understand and model the uncertainties of the mass balance, therefore providing a basis for future studies.