中文摘要：

以河南省封丘县为研究区，以环境协变量信息和先期获得的土壤数值化分类结果为基础数据源，在土壤分类距离空间自相关性分析的基础上，构建土壤分类距离一环境协变量空间回归模型，实施土壤分类距离空间预测，并最终实现研究区25m分辨率数字化土壤制图。输出结果表明，研究区5种主要土壤类型中，普通底锈干润雏形土分布面积最大、弱盐灌於干润雏形土次之，分布比例分别为36％和24％。结合确定性趋势距离和非确定性残差的空间变异特征，阐释了研究区土壤空间分布格局的发生学背景和随机性因素的影响。与基于随机模型的土壤预测制图相比，基于环境协变量空间回归模型的数字化土壤制图输出结果展示了相似的研究区土壤空间分布整体格局，且具有细节清晰、图斑边界自然的特点。一方面能更好地诠释土壤空间分布的连续性和渐变性特征；另一方面能较好地反映微域成土环境对土壤发生学特性空间变异特符的影响。

英文摘要：

Using the environmental information gathered and results of the numerical classification obtained by fuzzy c-means algorithm, regression models of soil taxonomic distance-environmental covariates were developed after spatial auto- correlation of taxonomic distance, to predict the taxonomic distances between soils at any spatial positions and the central profiles established in the study area. Based on the above prediction, a digital soil map with grid resolution of 25 m was produced after defuzzification and fragment-incorporation. It was clearly shown in the digital soil map that Typic Endorusti- ustic Cambosols was soil type with the biggest distribution percentage, covering about 36% of the total study area, while Parasalic Siltigi-ustic Cambosols, the soil type with the second biggest coverage, occupying around 24% of the land surface. By analysis on spatial variability of both determinable trend and undeterminable residual involved in soil taxonomic distance, the influences of soil-forming environment and stochastic factors on soil spatial variation were discussed. It was concluded that, compared to predictive map originated from a stochastic model, the digital map produced by the employment of soil taxonomic distance-environmental covariate regression model displayed a similar pattern of spatial soil distribution. However, much more information contained and reasonable delineation borders in the digital map was indicative of its capacity to demonstrate continuity and transition of soil distribution, and to explain spatial soil variation induced by microenvironmental factors and human activities.