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

作者：Baizhi Jiang, Wenyue Xu, Di Zhang, Fan Nie, Qi Sun · 发表于：Ecological Indicators · 年份：2021 · DOI：10.1016/j.ecolind.2021.108472 · 被引用次数：15 · 研究领域：Soil Geostatistics and Mapping、Land Use and Ecosystem Services、Spatial and Panel Data Analysis

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 th...