区域尺度下黄土高原土壤全钾含量的空间模拟

Spatial simulation of soil total potassium in regional scale for Loess Plateau Region

为了揭示高原区域地区尺度上土壤全钾的空间异质性及其影响因素,该文采用状态空间方程和传统线性回归模型对该区土壤全钾含量的空间分布进行了模拟,并分析了其与土壤体积质量、黏粒含量、粉粒含量、土壤酸度、降水、气温和海拔高度等因素之间的关系。结果表明,以上变量在30～50km的采样间距下均表现出较好的空间自相关性,其中土壤体积质量、黏粒含量、粉粒含量、降水和气温与土壤全钾之间存在显著的交互相关关系,可用于土壤全钾的状态空间模拟。不同因素组合下的状态空间方程均比使用相同变量的线性回归方程能更好的模拟土壤全钾含量的空间分布。使用土壤体积质量和黏粒含量的双因素状态空间方程模拟效果最好,决定系数R2为0.978,均方根误差(RMSE)为0.049。状态空间模拟在大尺度区域的应用表现出较好的效果,为研究该区其他土壤属性的空间异质性提供了参考。

To understand the spatial heterogeneity of soil total potassium in regional scale for Loess Plateau Region and its influencing factors, In the study, a total of 283 sampling sites were investigated in order to estimate the spatial variation of soil total potassium （STK） across the entire Loess Plateau （620,000 km 2 ）. Spatial simulation and classical linear regression were used to quantify the relationships between STK and bulk density, clay and silt content, soil pH, precipitation, temperature, and elevation. The best state-space models explained more than 97% of the STK variation, while the best linear regression model explained less than 26% of the STK variation. The results showed that all the state-space models described the spatial variation of STK much better than that of the corresponding linear regression models. Spatial simulation is recommended as a useful tool for quantifying spatial relationships between soil properties and the other environmental factors in large-scale regions.