Abstract. The status of the SOC stock at any position in the landscape is subject to a complex interplay of soil-state factors operating at different scales and regulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability and key drivers of SOC stock might be specific for subareas compared to those influencing the whole landscape. Consequently, separately calibrating models for subareas (local models) that collectively cover a target area can result in different prediction accuracy and SOC stock drivers compared to a single model (global model) that covers the whole area. The goal of this study was therefore to (1) assess how global and local models differ in predicting the humus layer, mineral soil and total SOC stock in Swedish forests, (2) identify the key factors for SOC stock prediction and their scale of influence. We use the Swedish National Forest Soil Inventory (NFSI) database and a digital soil mapping approach to evaluate the prediction performance using Random Forest modelling calibrated locally for the northern, central and southern Sweden (local models) and for the whole Sweden (global model). Models were built by considering (1) only site characteristics which are recorded on the plot during NFSI, (2) remotely sensed variables and (3) both site characteristics and remotely sensed variables. Local models are generally more effective for predicting SOC stock after testing on independent validation data. Using remotely sensed variables together with NFSI data indicates that such covariates have limited predictive strength but that site specific variables from the NFSI covariates show better explanatory strength for SOC stocks. The most important covariates that influence the humus layer, mineral soil and total SOC stock were related to the site characteristic covariates and include the soil moisture class, vegetation type, soil type and soil texture. Future studies could focus in mapping these influential site covariates which have potential for future SOC stock prediction models.

中文翻译：

---

使用遥感和特定地点变量预测瑞典森林中土壤有机碳储量的空间分布

摘要。在景观的任何位置上，SOC存量的状态都受到土壤状态因素在不同规模上运行并调节多个过程的复杂相互作用的影响，导致土壤充当净汇或净碳源。森林景观的特征是高度的空间变异性，与影响整个景观的森林相比，SOC存量的关键驱动力可能是针对子区域的。因此，与覆盖整个区域的单个模型（全局模型）相比，分别校准共同覆盖目标区域的子区域的模型（局部模型）可以导致不同的预测精度和SOC库存驱动器。因此，本研究的目的是（1）评估全球模型和局部模型在预测瑞典森林的腐殖质层，矿质土壤和总SOC存量方面的差异，（2）确定SOC库存预测的关键因素及其影响范围。我们使用瑞典国家森林土壤清单（NFSI）数据库和数字土壤制图方法，通过使用随机森林模型对瑞典北部，中部和南部（本地模型）以及整个瑞典（整体模型）进行局部校准来评估预测性能。通过考虑以下因素建立模型：（1）仅在NFSI期间记录在图中的站点特征；（2）遥感变量；（3）站点特征和遥感变量。在对独立验证数据进行测试之后，本地模型通常更有效地预测SOC库存。将遥感变量与NFSI数据一起使用表明，这些协变量的预测强度有限，但是NFSI协变量的特定于现场的变量对SOC储量显示出更好的解释强度。影响腐殖质层，矿质土壤和土壤有机碳总量的最重要的协变量与场地特征协变量有关，包括土壤湿度类别，植被类型，土壤类型和土壤质地。未来的研究可能会集中在绘制这些有影响力的站点协变量，这些协变量对于将来的SOC存量预测模型具有潜力。土壤类型和土壤质地。未来的研究可能会集中在绘制这些有影响力的站点协变量，这些协变量对于未来的SOC库存预测模型具有潜力。土壤类型和土壤质地。未来的研究可能会集中在绘制这些有影响力的站点协变量，这些协变量对于将来的SOC存量预测模型具有潜力。