Pierre Goovaerts

TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.

TL;DR: In this article, three multivariate geostatistical interpolation algorithms for incorporating a digital elevation model into the spatial prediction of rainfall are presented, i.e., simple kriging with varying local means, krigging with an external drift, and colocated cokriging.

TL;DR: An overview of the most recent developments in the field of geostatistics and describes their application to soil science can be found in this article, where the authors assess the uncertainty about unsampled values, which usually takes the form of a map of the probability of exceeding critical values, such as regulatory thresholds in soil pollution or criteria for soil quality.

TL;DR: In this article, the main applications of geostatistics to the description and modeling of the spatial variability of microbiological and physico-chemical soil properties are reviewed, and the key issue of fitting permissible models to experimental semivariograms is addressed for univariate and multivariate situations.

TL;DR: Two new criteria (exceedence probability plot and narrowness of probability intervals that include the true values) are presented to assess the accuracy and precision of local uncertainty models using cross-validation.