Mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: A case study in the Heihe River Basin, northwest China

Spatial prediction is an important approach to obtain location-specific values of soil electrical conductivity (EC), which is a proxy of soil salinity and important for agricultural management in arid and semi-arid areas. Linear regression models assume that the relation between soil EC and environmental covariates is constant over the area to be predicted. This is problematic at the regional scale, at which some of the regression parameters may indeed be globally constant, whereas others may vary locally. Moreover, model residuals often exhibit spatial dependence, which invalidates the ordinary least squares linear regression. This study examined the combination of a mixed geographically weighted regression model with simple kriging of the residuals (MGWGK) for mapping soil EC in the Heihe River Basin, an inland river basin in arid northwest China. We compared the performance of MGWRK with those of multiple linear regression (MLR), regression kriging (RK), geographically weighted regression (GWR), geographically weighted regression kriging (GWRK) and mixed geographically weighted regression (MGWR). Environmental covariates were developed from spatial information on topography, climate, vegetation, and geographic position. A ten-fold cross-validation was applied to evaluate predictive accuracy of the various methods. Soil EC ranged from 0.031 to 182.100 dS m−1, exhibiting a contrasting distribution of soil EC in the upper, middle, and lower river reaches. The MGWRK method outperformed other methods. The effects of different environmental covariates on soil EC were revealed by the fixed and geographically varying parameters of MGWRK. The nugget-to-sill ratios of fitted variogram models all fell between 25 and 32%, exhibiting moderate spatial autocorrelation of models residuals. Predictive accuracy was improved by MGWRK, as the spatial dependence of model residuals were included in the prediction. When selecting an optimal linear regression model, covariates should be tested to see if they are constant (as in MLR) or spatially varying (as in GWR) or semi-varying (as in MGWR), and model residuals should be tested for spatial dependence (as in MGWRK).