High-resolution maps of soil organic carbon for Abisko, northern Sweden

Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This contribution provides maps of SOC in a sub-arctic mountainous peatland environment in the discontinuous permafrost zone for the Stordalen area in the Abisko region, northern Sweden. Four machine-learning techniques were evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1 × 1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0-150 cm) is estimated to 8.3 ± 8.0 kg C m-2 and the SOC stored in the top meter (0-100 cm) to 7.7 ± 6.2 kg C m-2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2 m, 10 m, 30 m, 100 m, 250 m and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions >30 m. This is associated with the occurrence of many small scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.储存在北部泥炭地与受永久冻土影响土壤中的土壤有机碳（Soil Organic Carbon, SOC）是全球碳循环的关键组成部分。本数据集提供了瑞典北部阿比斯科（Abisko）地区斯托达伦（Stordalen）区域内，不连续永久冻土带中亚北极山地泥炭地环境下的SOC分布图。本研究评估了四种机器学习技术用于SOC定量：多元线性回归、人工神经网络、支持向量机与随机森林。其中随机森林模型表现最优，被用于以1米（1×1米）的空间分辨率，针对多个深度分层预测SOC储量。本研究生成的高分辨率（1米）土地覆盖分类数据，是相关性最高的预测变量。 该区域0-150厘米深度的平均SOC储量约为8.3±8.0 kg C m⁻²，0-100厘米表层土壤的SOC储量约为7.7±6.2 kg C m⁻²。预测模型结果显示，湿地区域尤其是泥炭高原，对区域SOC储量具有相对重要的贡献。研究还针对2米、10米、30米、100米、250米及1000米的降低空间分辨率场景预测了总SOC储量，结果表明：当空间分辨率大于30米时，土地覆盖类别的细节会显著丢失，且SOC储量存在被低估的趋势。这一现象与大量小规模湿地的存在密切相关：这些湿地形成了SOC储量的局部热点区域，但在低分辨率下会被忽略。 与土地覆盖及永久冻土分布相关的SOC储量剧烈变化，是本研究面临的最具挑战性的方法学问题。然而，在本研究的局地、区域及环北极尺度下，限制可靠SOC制图工作开展的核心因素是覆盖全环境梯度范围的土壤剖面（soil pedon）数据极度匮乏。针对阿比斯科地区，既往的SOC与永久冻土动态研究表明，该区域绝大多数SOC的形成年限不足2000年，且处于高度动态变化中。未来的研究需要探讨永久冻土退化的地貌响应，以及后永久冻土景观中所有景观单元内的SOC归宿。