Spatial interpolation methods applied in the environmental sciences: A review

• Published in: Environmental modelling & software : with environment data news Vol. 53; pp. 173 - 189
• Main Authors: Li, Jin, Heap, Andrew D.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-03-2014; Elsevier
• Subjects: Analysis; Animal, plant and microbial ecology; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Geostatistics; Interpolator; Kriging; Methods and techniques (sampling, tagging, trapping, modelling...); Spatially continuous surfaces; Statistics; Interpolator; Spatially continuous surfaces; Statistics; Analysis; Kriging; Geostatistics; Interpolation; Statistical analysis; Continuous; Environmental sciences; Method
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2013.12.008

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.
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•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.