Prediction of soil properties by using geographically weighted regression at a regional scale

Abstract

Detailed information about spatial distribution of soil properties is important in ecological modelling, environmental prediction, precision agriculture, and natural resources management, as well as land-use planning. In the present study, a recently developed method called geographically weighted regression (GWR) is applied to predict spatial distribution of soil properties (pH, soil organic matter, available nitrogen, available potassium) based on topographical indicators, climate factors, and geological stratum at a regional scale. In total, 1914 soil samples collected from a depth of 0–20cm were used to calibrate and validate the models. Performances of the GWR models were compared with the traditional, ordinary least-squares (OLS) regression. The results indicated that the GWR models made significant improvements to model performances over OLS regression, based on F-test, coefficient of determination, and corrected Akaike information criterion. GWR models also improved the reliability of the soil–environment relationships by reducing the spatial autocorrelations in model residuals. Meanwhile, the use of GWR models disclosed that the relationships between soil properties and environmental variables were not invariant over space but exhibited significant spatial non-stationarity. Accordingly, the GWR models remarkably improved the prediction accuracies over the corresponding OLS models. The results demonstrated that GWR could serve as a useful tool for digital soil mapping in areas with complex terrain.