Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with fine (FR) panchromatic (PAN) image. Pansharpening is technique applied to such sensor images generate an FR MS by injecting detail taken from the PAN while simultaneously preserving spectral information of methods are mostly on per-pixel basis and use extract detail....

Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or "land use" regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality by land use regression and the spatial...

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e.,...

A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. ...

Prediction of soil organic carbon (SOC) at unsampled locations is central to statistical modeling of regional SOC stocks. This is often accomplished by applying geostatistical techniques to plot inventory data. However, in many cases inventory data is sparsely sampled (<0.1 plots/km) relative to the region of interest, and it is unknown if geostatistics provides any advantage. Our objective was...

As the development of earth sciences and cross-disciplinary, it’s more and more meaningful and valuable to analyze and estimate precipitation spatial distribution. Precipitation spatial information is important in many fields, such as water resource management, drought and flood disaster predication, and regional sustainable development. It’s unrealistic to get the accurate predication of a par...

Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. However, few interpolation methods perform satisfactorily for complex terrains. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. A data set of 153...

Ground station temperature data are not commonly used simultaneously with the Advanced Very High Resolution Radiometer (AVHRR) to model and predict air temperature or land surface temperature. Technology was developed to acquire near-synchronous datasets over a 1 000 000 km region with the goal of improving the measurement of air temperature at the surface. This study compares several statistic...

Surrogate-based optimization has become popular in the design of complex engineering systems. Each optimization cycle consists of analyzing a number of designs, fitting a surrogate, performing optimization based on the surrogate, and finally performing exact simulation at the design obtained by the optimization. Adaptive sampling algorithms that add one point per cycle are readily available in ...

The benefits of an integrated geographical information system (GIS) and a geostatistics approach to accurately model the spatial distribution pattern of precipitation are known. However, the determination of the most appropriate geostatistical algorithm for each case is usually neglected, i.e. it is important to select the best interpolation technique for each study area to obtain accurate resu...