The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without an...

Abstract Cross-validation and performance measures are standard components in the evaluation of a geostatistical model. These well established univariate case, but for multivariate modeling have not received as much attention. In case single target variable, approaches remain valid, fully where vector variables needs to be estimated, based on all estimates simultaneously. An extension cross-val...

In this technical note, a geostatistical model was applied to explore the spatial distribution of source rock data in terms total organic carbon weight concentration. The median polish kriging method used approximate “row and column effect” generated array data, order for ordinary methodology be by means residuals. Moreover, sequential Gaussian simulation employed quantify uncertainty estimates...

Diagnosis of soil salinity and characterizing its spatial variability both vertically and horizontally are needed to establish control measures in irrigated agriculture. In this regard, it is essential that salinity development in varying soil depths be known temporally and spatially. Apparent soil electrical conductivity, measured by electromagnetic induction instruments, has been widely used ...

Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatis...

Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S…x) based on observations Yi ˆ S…xi † ‡ Zi at sampling locations xi , where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly...