The spatial modelling of soil properties provides us with essential and useful information relevant to soil fertility management and environmental protection. The study aims to investigate the ability of empirical Bayesian kriging and principal component analysis, multiple linear regressions with environmental covariates in the modelling of soil properties distribution. For this study, thirty (n = 30) soil samples were obtained at 0–30 cm depth and nine (9) soil-environmental covariates derived from the digital elevation model (Shutter Radar Topography Mission at 30 m resolution) in southeastern Nigeria. The summary statistics revealed high sand content (> 70%) which revealed that the soils of the humid tropics developed on the coastal plain parent material are coarse-textured. Pearson correlation matrix revealed a significant but weak correlation between soil properties and soil-environmental variables. Using empirical Bayesian kriging interpolation, the cross-validation results revealed an acceptable prediction for magnesium, potassium, phosphorus, pH and total nitrogen (R² > 0.5 with RMSE closer to 0). The principal component analysis reveals that principal component 1 to principal component 5 could interpret 78.1% of the total variability of soil properties. Modelling each soil property using multiple linear regression with the derived soil-environmental covariates, the study noted that only magnesium gave the best model fit with 50.9% of the soil-environmental covariates explaining its variability, while other soil properties presented unacceptable models. Therefore, to improve soil property prediction through multiple linear regression, more observation points are recommended to interpret better the performance of multiple linear regression over flat terrain system.

土壤特性的空间建模为我们提供了与土壤肥力管理和环境保护有关的必要和有用的信息。该研究旨在调查经验贝叶斯克里金法和主成分分析，具有环境协变量的多元线性回归在土壤特性分布建模中的能力。在本研究中，三十（n = 30）在0–30 cm深度处获得土壤样品，并从尼日利亚东南部的数字高程模型（分辨率为30 m的快门雷达地形任务）获得九（9）个土壤-环境协变量。汇总统计数据表明，含沙量较高（> 70％），这表明在沿海平原母体材料上发育的热带湿润土壤质地粗糙。皮尔逊相关矩阵揭示了土壤特性与土壤环境变量之间的显着但微弱的相关性。使用经验贝叶斯克里格插值，交叉验证的结果表明镁，钾，磷，pH和总氮（R ² > 0.5，RMSE接近0）。主成分分析表明，主成分1至主成分5可以解释土壤性质总变异的78.1％。使用多元线性回归对衍生的土壤-环境协变量进行建模，对每种土壤特性进行建模，研究指出，只有镁能提供最佳模型拟合，其中50.9％的土壤-环境协变量说明了其变异性，而其他土壤特性则表现出不可接受的模型。因此，为了通过多元线性回归改善土壤性质预测，建议更多观察点来更好地解释平坦地形系统上多元线性回归的性能。