Contrasting multiple deterministic interpolation responses to different spatial scale in prediction soil organic carbon: A case study in Mollisols regions

Abstract

Accurate estimation of SOM content of Mollisols influences fundamental sub-micron to global-scale biogeochemical processes and carbon-climate feedbacks. Comparative analysis of the responses of Multiple deterministic Interpolation models to spatial scale variations is the basis for multiscale soil organic carbon (SOC) pools simulation and evaluation. This study mainly manifested as spatial variation at different analytical levels of spatial correlation and the change of characteristic data attributes that characterize this change. Three spatial scales were selected (HQ, endemic area; XF, local area; JX, most area). A set of 164 topsoils (0–20 cm, sampling density of 0.1 points / km2) samples were taken, and 14 environmental variables and 14 soil characters variables were employed to contrast prediction accuracy of ordinary kriging (OK), inverse distance weighting (IDW), radial basis function (RBF), global polynomial (GPI), local polynomial (LPI), regression kriging interpolation methods such as (RK) and geographically weighted regression kriging (GWRK) responses to different spatial scale. Using a cross-validation procedure to evaluate the performance of the models. Overall, RK and GWRK, due to the introduction of the auxiliary variables, effectively predict SOM content and the improvement of prediction accuracy depends on the spatial scale, compared with IDW, RBF, GPI, and LPI.Furthermore, soil characters variables (TN, pH) as covariates have a more significant impact on the final prediction than environmental variables (Elevation, Slope, etc.). Therefore, the mutations and trends associated with the spatial resolution are analyzed in different spaces. The root means square error (RMSE) of RK and GWRK are reduced by 46.66 % and 64.26 % compared with OK. RK with fixed the regression coefficient obtained by OLS reveals that the uniformity of the object distribution in HQ and XF have Adjusted R2 0.955 and 0.915. Otherwise, the RI of GWRK with 0.792 adjusted R2 in JX is relatively 17.6 % higher than RK. The results from our case study highlight a key role in selecting the optimal spatial interpolation method for a given dataset associated with spatial scales and analyzing their regularity and applicability on different spatial scales.