https://pubag.nal.usda.gov/download/26859/pdf

Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics

A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided. Display Omitted Comparison of commonly used spatial interpolation methods in environmental science.Analysis of factors affecting the performance of spatial interpolation methods.Classification of 25 methods to illustrate their relationship.Guidelines for selecting an appropriate method for a given dataset.A list of software packages for commonly used spatial interpolation methods.

Review: Spatial interpolation methods applied in the environmental sciences: A review

A factorial, computational experiment was conducted to compare the spatial interpolation accuracy of ordinary and universal kriging and two types of inverse squared-distance weighting. The experiment considered, in addition to these four interpolation methods, the effects of four data and sampling characteristics: surface type, sampling pattern, noise level, and strength of small-scale spatial correlation. Interpolation accuracy was measured by the natural logarithm of the mean squared interpolation error. Main effects of all five factors, all two-factor interactions, and several three-factor interactions were highly statistically significant. Among numerous findings, the most striking was that the two kriging methods were substantially superior to the inverse distance weighting methods over all levels of surface type, sampling pattern, noise, and correlation.

An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting

http://mmc2.geofisica.unam.mx/cursos///gest/Articulos/Geostatistics/Modelling%20soil%20variation%20past,%20present,%20and%20future.pdf

Modelling soil variation: past, present, and future

SUMMARY
To support future decisions on alternative strategies of updating soil survey information, we have studied the efficiency of four strategies, viz. revision, upgrading, revision plus upgrading and upgrading by two-phase sampling. Revision results in a new soil map. Upgrading gives statistical information about means and variances within mapping units of the soil map.

The merits of these strategies were measured in terms of the increase in accuracies of the spatial estimates of the values at unvisited points and of the spatial means of a study area of several soil characteristics. Point values were estimated by assigning an estimate of the mean value to any point in a given mapping unit. In the first strategy, the estimator was derived from the representative profile description of that mapping unit. In the other strategies, the estimator was derived from the statistical sample.

Although the mapping units of the revised map were more homogeneous for some characteristics, the point estimates using the values of the representative profile description were no more accurate. This was due to the bias of this estimator, which rules out the reduction in spatial variance. If revision was followed by sampling this bias could be eliminated. As a result, for some characteristics the point estimates became more accurate than those based on the original map.

Estimates of the spatial means of the study area via upgrading by two-phase sampling were more accurate, for all characteristics, than estimates via revision plus upgrading. Using the estimates of the spatial variance within the mapping units of the original map for allocation was apparently more effective than reducing the variance itself.

Strategies for updating soil survey information: a case study to estimate phosphate sorption characteristics

A study was designed to compare the performance of six spatial interpolation methods to estimate soil properties at unvisited points. These methods were global mean, moving average, nearest neighbour, inverse squared distance, Laplacian smoothing splines and ordinary point kriging. These methods were also applied in combination with a choropleth map (soil map) by stratifying the area. The soil properties estimated were thickness of A1 horizon, maximum areic mass of phosphate adsorbed by soil, mean highest water table and mean lowest water table. The performance of the methods was measured by estimating the spatial means of the squared and absolute error (quality criteria not conditional on the sample of test points) by a stratified simple random sample of test points. The mean squared error was very large in proportion to the spatial variation over the total area for all methods and properties. Differences between methods were small. In general, no statistically significant stratification or weighting effects were found. The effect of weighting plus stratification was usually not significant either. Overall, weighting with inverse squared distance was as satisfactory as weighting by ordinary point kriging. However, the latter was superior near data points. Also, when combined with soil map stratification, kriging was more reliable in the sense that it estimated all properties well. Estimates obtained using the means of six soil map units were better, although not significantly, than those obtained from unstratified kriging and as good as kriging within three map units.

The performance of spatial interpolation methods and choropleth maps to estimate properties at points: a soil survey case study.

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Strategies for updating soil survey information: a case study to estimate phosphate sorption characteristics

The performance of spatial interpolation methods and choropleth maps to estimate properties at points: a soil survey case study.