Predicting the Influence of Multi-Scale Spatial Autocorrelation on Soil–Landform Modeling

Document Type

Article

Publication Date

4-22-2016

Publication Source

Soil Science Society of America

Abstract

Although numerous soil–landform modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, we quantified the SAC inherent in soil and landform variables of four widely divergent pedogeomorphological systems around the world to examine general relationships between SAC and spatial regression model results. Spatial regressions were performed by incorporating spatial filters, extracted by spatial eigenvector mapping, into non-spatial models as additional predictor variables. Results indicated that incorporation of spatial filters improved the performance of the non-spatial regressions—increases in R2 and decreases in both Akaike Information Criterion (AIC) and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each soil variable. Our findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a soil property when treated as a response variable. Thus, the level of SAC present in a soil variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account.

Inclusive pages

409-419

ISBN/ISSN

0361-5995

Comments

Permission documentation on file.

Publisher

Soil Science Society of America and American Society of Agronomy and Crop Science Society of America

Volume

80

Peer Reviewed

yes

Issue

2

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