Kriging, in the scientific literature, is used as a name for the theory of prediction in random processes (random fields) with an unknown mean value and, possibly, with an unknown covariance function. M. Stein in a series of articles (1988), (1990a), (1990b) and (1990c) studies the case when the unknown covariance function of the observed process is misspecified, but not estimated from the data...

In Precision Viticulture, the increasing number of information sources calls for methods to combine them and to generate information for professionals. Each data is located on its own geographical support, and geostatistics are mostly used to produce estimates on a common grid. Estimated values have to be associated with confidence intervals; using kriging variance to compute them is generally ...

soil temperature is one of the most effective parameters for plant growth. although the spatiotemporal monitoring of soil temperature is important for providing an optimum condition to maximize the yield efficiency, indirect measurements for zoning  of soil temperature are essential due to difficulties in direct methods of measuring it and also the limited measurement points. thus, the efficien...

and Salas (1985) compared kriging with several other interpolation techniques, including inverse distance, for The choice of an optimal interpolation technique for estimating annual precipitation distributions and found kriging to soil properties at unsampled locations is an important issue in sitebe superior to inverse distance weighting. Warrick et specific management. The objective of this s...

The method of stochastic simulation is proposed to model the atmospheric effect on InSAR measurements based on sample data. Test results show that 37.44% reduction in the standard devisation of the atmospheric errors can be achieved with the method of stochastic simulation, compared to 25.69% with the method of Kriging interpolator. The relative improvement of the former over the latter amounts...

     Drought monitoring is a fundamental component of drought risk management. It is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolation of spatially referenced data and the prediction of v...

Spatial information surveyed by photogrammetry, airborne LiDAR and Mobile Measurement System (MMS) above ground level can be analyzed by scientists using standard geostatistical methodologies such as ordinary Kriging and sequential Gaussian simulation to interpolate heterogeneities of profiles from sparse sample data. Proven effective by researchers, the Kriging algorithm model is used by comme...

Traditional clustering methods assume that there is no measurement error, or uncertainty, associated with data. Often, however, real world applications require treatment of data that have such errors. In the presence of measurement errors, well-known clustering methods like k-means and hierarchical clustering may not produce satisfactory results. The fundamental question addressed in this paper...

The magnitude of kriging errors varies in accordance with the surface properties. The purpose of this paper is to determine the association of ordinary kriging (OK) estimated errors with the local variability of surface roughness, and to analyse the suitability of probabilistic models for predicting the magnitude of OK errors from surface parameters. This task includes determining the terrain p...