Soil surveys and mapping with traditional methods are time-consuming and expensive especially in mountainous areas while demand for detailed soil information is steadily increasing. This study tested two spatial hybrid approaches to predict and map basic soil properties using high resolution digital elevation model (DEM) and multispectral satellite imagery in a study area located in the Caucasus Mountains, Azerbaijan. Terrain attributes and spectral indices extracted from DEM with 12.5 m spatial resolution and Pléiades-1 data were used as auxiliary variables. A total of 115 soil samples were collected from the surface layer of 423 ha area and tested for soil organic carbon, soil reaction (pH in H₂O and KCl solutions), calcium carbonate (CaCO₃), sand, silt, clay and hygroscopic water content. The predictive capability of Universal Kriging (UK) and Random Forest Kriging (RFK) was evaluated using spatial cross-validation technique. To model and quantify the associated uncertainty of these models a probabilistic framework, kriging variance approach was applied. The uncertainty models were validated using independent and randomly selected control points (20% of the reference samples). For this, the actual fraction of true values falling within symmetric prediction intervals was calculated and visualized known as accuracy plot. Although the performances of the tested models were similar, RFK was superior in view of both accuracy and computed biases. The models were capable of delineating spatial pattern, mostly elevation dependent as well as the local patterns attributed by e.g., variations in vegetation, land use and soil erosion. UK model produced a few local erratic spatial patterns (e.g., in the case of pH) corresponding to the artifacts such as roads and houses in the image that should be considered in future applications. When comparing the uncertainties, both the models produced considerable underestimations and overestimations depending on soil property. RFK provided better uncertainty estimation for the most of soil properties than UK, the latter technique was more appropriate for the clay and pH_KCl prediction. This case study confirmed the importance of assumptions made in uncertainty modelling and quantification. Those soil properties were therefore reliably predicted that their residuals were compatible with the normality assumption and showed apparent spatial correlation, e.g., both the models severely overestimated uncertainty of CaCO₃ due to lack of normality assumption and low spatial correlation. This study showed that high resolution remote sensing data are promising, and the procedure presented in this study can be reliably used to map the studied soil properties and extended to partially larger adjacent areas characterized by similar environmental conditions in the Caucasus Mountains. However, with respect to future digital soil mapping, we assume that it is important to consider sampling design, testing other modelling approaches their uncertainties and multi-scale digital terrain analysis as well.

使用传统方法进行土壤调查和制图既费时又费钱，尤其是在山区，同时对详细土壤信息的需求也在稳步增长。本研究测试了两种空间混合方法，使用高分辨率数字高程模型 (DEM) 和多光谱卫星图像在位于阿塞拜疆高加索山脉的研究区预测和绘制基本土壤特性。从具有 12.5 m 空间分辨率的 DEM 和 Pléiades-1 数据中提取的地形属性和光谱指数用作辅助变量。从 423 公顷区域的表层收集了总共 115 个土壤样品，并测试了土壤有机碳、土壤反应（H ₂ O 和 KCl 溶液中的pH 值）、碳酸钙（CaCO ₃)、沙子、淤泥、粘土和吸湿性含水量。使用空间交叉验证技术评估了通用克里金法 (UK) 和随机森林克里金法 (RFK) 的预测能力。为了对这些模型的相关不确定性进行建模和量化，应用了概率框架，克里金方差法。使用独立和随机选择的控制点（参考样本的 20%）验证不确定性模型。为此，计算并可视化了落在对称预测区间内的真实值的实际比例，称为精度图。尽管测试模型的性能相似，但考虑到准确性和计算偏差，RFK 更胜一筹。这些模型能够描绘空间模式，主要取决于高程以及由例如归因于的局部模式，植被、土地利用和土壤侵蚀的变化。UK 模型产生了一些局部不稳定的空间模式（例如，在 pH 值的情况下），对应于图像中的道路和房屋等伪影，在未来的应用中应予以考虑。在比较不确定性时，两种模型都产生了相当大的低估和高估，具体取决于土壤性质。RFK 为大多数土壤特性提供了比 UK 更好的不确定性估计，后一种技术更适合粘土和 pH 值 根据土壤性质，这两种模型都产生了相当大的低估和高估。RFK 为大多数土壤特性提供了比 UK 更好的不确定性估计，后一种技术更适合粘土和 pH 值 根据土壤性质，这两种模型都产生了相当大的低估和高估。RFK 为大多数土壤特性提供了比 UK 更好的不确定性估计，后一种技术更适合粘土和 pH 值_氯化钾预测。该案例研究证实了在不确定性建模和量化中所做假设的重要性。因此，可靠地预测了这些土壤特性，它们的残差与正态性假设一致，并显示出明显的空间相关性，例如，两个模型都严重高估了 CaCO _{3 的}不确定性由于缺乏正态性假设和低空间相关性。这项研究表明高分辨率遥感数据很有前景，本研究中提出的程序可以可靠地用于绘制所研究的土壤特性，并扩展到高加索山脉具有相似环境条件的部分较大的相邻区域。然而，对于未来的数字土壤测绘，我们认为考虑采样设计、测试其他建模方法的不确定性和多尺度数字地形分析也很重要。