Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland

It is challenging to perform spatial geochemical modelling due to the spatial heterogeneity features of geochemical variables. Meanwhile, high quality geochemical maps are needed for better environmental management. Soil organic C (SOC) distribution maps are required for improvements in soil management and for the estimation of C stocks at regional scales. This study investigates the use of a geographically weighted regression (GWR) method for the spatial modelling of SOC in Ireland. A total of 1310 samples of SOC data were extracted from the National Soil Database of Ireland. Environmental factors of rainfall, land cover and soil type were investigated and included as the independent variables to establish the GWR model. The GWR provided comparable and reasonable results with the other chosen methods of ordinary kriging (OK), inverse distance weighted (IDW) and multiple linear regression (MLR). The SOC map produced using the GWR model showed clear spatial patterns influenced by environmental factors and the smoothing effect of spatial interpolation was reduced. This study has demonstrated that GWR provides a promising method for spatial geochemical modelling of SOC and potentially other geochemical parameters.

► Spatial geochemical modelling can be achieved using geographically weighted regression (GWR).
► The resulting soil organic carbon (SOC) map showed clear spatial patterns influenced by environmental factors.
► The smoothing effect of spatial interpolation by GWR was reduced.
► The GWR provides a promising method for spatial geochemical modelling of SOC and potentially other geochemical parameters.