Large-scale digital soil maps are essential for rational and sustainable land management as well as accurate fertilizer application. This study focuses on digital mapping of soil properties, namely soil organic carbon (SOC), pH, nitrogen (N), phosphorus (P), and potassium (K) in Chernozem topsoil and subsoil. The study was conducted on two arable fields in the Cis-Ural forest-steppe zone of the Republic of Bashkortostan (Russia). The random forest algorithm in combination with terrain attributes and Sentinel-2A satellite data was applied for spatial prediction of soil properties. The root-mean-square error (RMSE) and coefficient of determination (R²) were used to determine the model performance. According to the Pearson correlation, a significant positive relationship between SOC and N content was found at all sites and depths (R = 0.76–0.92). A cross-validation revealed that SOC (R² = 0.22–0.62, RMSE = 0.35–0.89%) and N (R² = 0.16–0.60, RMSE = 21.11–36.6 mg kg⁻¹) were best predicted among other properties at all depths using remote sensing data, whereas the performance of predictive models decreased with depth. However, a relationship between the content of some soil properties and their spatial distribution at study depths was observed, which can be used as an additional explanatory variable. We suppose that digital mapping of soil properties at the arable field scale should not be limited to topographic and remote sensing variables. Based on this information, the use of auxiliary variables, such as collocated soil information in combination with relief and remote sensing data can be effective in more accurately estimating the spatial distribution of properties across arable fields at different depths. Overall, this study provides valuable insights into spatial modelling of the vertical distribution of soil properties, highlighting the significance of remote sensing data at the arable field scale. The findings can be valuable for land managers, agronomists, and policymakers seeking sustainable land management practices and efficient fertilizer application, as well as for developing further mapping procedures for arable fields.

大规模数字土壤图对于合理、可持续的土地管理以及准确的施肥至关重要。本研究重点关注土壤特性的数字制图，即黑钙土表土和底土中的土壤有机碳 (SOC)、pH、氮 (N)、磷 (P) 和钾 (K)。这项研究是在巴什科尔托斯坦共和国（俄罗斯）的顺乌拉尔森林草原区的两块耕地上进行的。应用随机森林算法结合地形属性和Sentinel-2A卫星数据对土壤性质进行空间预测。均方根误差 (RMSE) 和决定系数 (R ² ) 用于确定模型性能。根据 Pearson 相关性，在所有地点和深度都发现 SOC 和 N 含量之间存在显着的正相关关系（R = 0.76-0.92）。交叉验证显示，在其他属性中，SOC（R ²  = 0.22–0.62，RMSE = 0.35–0.89%）和 N（R ²  = 0.16–0.60，RMSE = 21.11–36.6 mg kg ⁻¹）的预测效果最好使用遥感数据预测深度，而预测模型的性能随着深度的增加而降低。然而，观察到一些土壤性质的含量与其在研究深度的空间分布之间的关系，这可以用作额外的解释变量。我们认为，耕地尺度土壤特性的数字制图不应仅限于地形和遥感变量。基于这些信息，使用辅助变量，例如结合地形和遥感数据的并置土壤信息，可以有效地更准确地估计不同深度耕地的属性空间分布。总体而言，这项研究为土壤性质垂直分布的空间建模提供了有价值的见解，强调了遥感数据在耕地尺度上的重要性。这些研究结果对于寻求可持续土地管理实践和高效施肥的土地管理者、农学家和政策制定者以及制定进一步的耕地测绘程序非常有价值。