The status of the soil organic carbon (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 sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas (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 and (2) identify the key factors for SOC stock prediction and their scale of influence.We used the Swedish National Forest Soil Inventory (NFSI) database and a digital soil mapping approach to evaluate the prediction performance using random forest models calibrated locally for the northern, central, and southern Sweden (local models) and for the whole of Sweden (global model). Models were built by considering (1) only site characteristics which are recorded on the plot during the NFSI, (2) the group of covariates (remote sensing, historical land use data, etc.) and (3) both site characteristics and group of covariates consisting mostly of remote sensing data.Local models were generally more effective for predicting SOC stock after testing on independent validation data. Using the group of covariates together with NFSI data indicated that such covariates have limited predictive strength but that site-specific covariates from the NFSI showed better explanatory strength for SOC stocks. The most important covariates that influence the humus layer, mineral soil (0–50 cm), and total SOC stock were related to the site-characteristic covariates and include the soil moisture class, vegetation type, soil type, and soil texture. This study showed that local calibration has the potential to improve prediction accuracy, which will vary depending on the type of available covariates.

中文翻译：

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使用一组协变量和特定地点数据预测瑞典森林土壤有机碳储量的空间分布

景观中任何位置的土壤有机碳 (SOC) 储量的状态受土壤状态因素的复杂相互作用的影响，这些因素在不同尺度上运行并调节多个过程，导致土壤充当净碳汇或净碳源。森林景观的特点是空间可变性高，与影响整个景观的那些因素相比，SOC 储量的关键驱动因素可能特定于子区域。因此，与覆盖整个区域的单个模型（全局模型）相比，单独校准共同覆盖目标区域的子区域模型（局部模型）可能会导致不同的预测精度和 SOC 存量驱动因素。因此，本研究的目标是 (1) 评估全球和局部模型在预测腐殖质层、矿质土壤、和瑞典森林中的总 SOC 储量和 (2) 确定 SOC 储量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来评估预测性能，使用随机为瑞典北部、中部和南部（当地模型）和整个瑞典（全球模型）本地校准的森林模型。通过考虑 (1) 仅考虑在 NFSI 期间记录在地块上的场地特征，(2) 协变量组（遥感、历史土地利用数据等）和 (3) 场地特征和协变量主要由遥感数据组成。在对独立验证数据进行测试后，本地模型通常更有效地预测 SOC 存量。将协变量组与 NFSI 数据一起使用表明，此类协变量的预测强度有限，但来自 NFSI 的特定地点协变量对 SOC 储量显示出更好的解释强度。影响腐殖质层、矿质土壤（0-50 厘米）和总 SOC 储量的最重要协变量与立地特征协变量有关，包括土壤湿度等级、植被类型、土壤类型和土壤质地。这项研究表明，局部校准有可能提高预测精度，这取决于可用协变量的类型。矿质土壤（0-50 厘米）和总 SOC 储量与立地特征协变量相关，包括土壤湿度等级、植被类型、土壤类型和土壤质地。这项研究表明，局部校准有可能提高预测精度，这取决于可用协变量的类型。矿质土壤（0-50 厘米）和总 SOC 储量与立地特征协变量相关，包括土壤湿度等级、植被类型、土壤类型和土壤质地。这项研究表明，局部校准有可能提高预测精度，这取决于可用协变量的类型。