Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon

Abstract

Geographically weighted regression kriging (GWRK) is a popular interpolation method, considering not only spatial parametric non-stationarity and relationship between target and explanatory variables, but also spatial autocorrelation of residuals. However, little attention has been paid to the effects of different sampling densities on GWRK technique for estimating soil properties. Objectives of this study were: (i) comparing the GWRK predictions with those obtained from multiple linear regression kriging (MLRK) and ordinary kriging (OK), and (ii) examining how different sampling densities affect the performance of GWRK for predicting soil organic carbon (SOC). Soil samples were simulated with four sampling densities, including 0.010, 0.020, 0.041, and 0.082 sites/km2. The results showed that GWRK made less prediction errors and outperformed MLRK and OK in the case of a high sampling density, with the root mean squared errors of GWRK<MLRK<OK and coefficient of determination of GWRK>MLRK>OK. However, in the case of a low sampling density, GWRK generated larger prediction errors, exhibiting a poorer performance than MLRK and OK. Accordingly, we conclude that GWRK can be considered as the best approach for predicting SOC in these three approaches with sufficient data points, but it has a poorer performance than the other methods with sparse data points.