Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.

中文翻译：

---

包括土壤空间邻域信息，用于数字土壤制图


数字土壤测绘 （DSM） 正在改变我们理解和管理土壤资源的方式，提供对应对环境挑战至关重要的高分辨率时空土壤信息。环境协变量的整合提高了土壤绘图的准确性，而相邻土壤样本数据的潜力在很大程度上被忽视了。本研究介绍了土壤空间邻域信息 （SSNI） 作为一种增强空间模型预测能力的新方法。利用来自 LUCAS Soil 和 Meuse 的两个开放访问数据集，我们的研究结果表明，结合 SSNI 提高了随机森林模型在绘制土壤有机碳密度（降低 %RMSE 为 3.1%）、镉（降低 %RMSE 为 3.6%）、铜（降低 %RMSE 为 5.9%）、铅（降低 %RMSE 为 11.5%）和锌（降低 %RMSE 为 7.4%）方面的准确性。与包含缓冲距离或倾斜地理坐标进行建模相比，SSNI 在 LUCAS Soil 和 Meuse 数据集中也表现更好。本研究强调了 SSNI 通过捕获邻近信息来改进数字土壤地图的价值。接受 SSNI 可能会导致在土壤管理方面做出更明智的决策，其在其他学科中的潜在适用性也仍有待未来研究工作的探索。