Spatial soil information in forests is crucial to assess ecosystem services such as carbon storage, water purification or biodiversity. However, spatially continuous information on soil properties at adequate resolution is rare in forested areas, especially in mountain regions. Therefore, we aimed to build high-resolution soil property maps for pH, soil organic carbon, clay, sand, gravel and soil density for six depth intervals as well as for soil thickness for the entire forested area of Switzerland. We used legacy data from 2071 soil profiles and evaluated six different modelling approaches of digital soil mapping, namely lasso, robust external-drift kriging, geoadditive modelling, quantile regression forest (QRF), cubist and support vector machines. Moreover, we combined the predictions of the individual models by applying a weighted model averaging approach. All models were built from a large set of potential covariates which included e.g. multi-scale terrain attributes and remote sensing data characterizing vegetation cover.

Model performances, evaluated against an independent dataset were similar for all methods. However, QRF achieved the best prediction performance in most cases (18 out of 37 models), while model averaging outperformed the individual models in five cases. For the final soil property maps we therefore used the QRF predictions. Prediction performance showed large differences for the individual soil properties. While for fine earth density the R² of QRF varied between 0.51 and 0.64 across all depth intervals, soil organic carbon content was more difficult to predict (R² = 0.19–0.32). Since QRF was used for map prediction, we assessed the 90% prediction intervals from which we derived uncertainty maps. The latter are valuable to better interpret the predictions and provide guidance for future mapping campaigns to improve the soil maps.

森林中的空间土壤信息对于评估碳储存、水净化或生物多样性等生态系统服务至关重要。然而，在森林地区，尤其是山区，很少有足够分辨率的土壤特性的空间连续信息。因此，我们的目标是为六个深度间隔以及整个瑞士森林地区的土壤厚度构建高分辨率土壤特性图，包括 pH 值、土壤有机碳、粘土、沙子、砾石和土壤密度。我们使用了 2071 个土壤剖面的遗留数据，并评估了数字土壤制图的六种不同建模方法，即套索、稳健的外部漂移克里金法、地理可加性建模、分位数回归森林 (QRF)、立体派和支持向量机。而且，我们通过应用加权模型平均方法结合了各个模型的预测。所有模型都是从大量潜在协变量构建的，其中包括例如多尺度地形属性和表征植被覆盖的遥感数据。

针对独立数据集评估的模型性能对于所有方法都是相似的。然而，QRF 在大多数情况下（37 个模型中的 18 个）实现了最佳预测性能，而模型平均在 5 个情况下优于单个模型。因此，对于最终的土壤特性图，我们使用了 QRF 预测。预测性能显示出个体土壤特性的巨大差异。虽然对于细土密度，QRF的 R ²在所有深度区间内在 0.51 和 0.64 之间变化，但土壤有机碳含量更难预测（R ² = 0.19–0.32）。由于 QRF 用于地图预测，我们评估了 90% 的预测区间，从中我们可以得出不确定性地图。后者对于更好地解释预测并为未来的绘图活动提供指导以改进土壤图很有价值。