Draining peatlands for agriculture transforms them into significant carbon (C) sources. Restoring drained peatlands is increasingly recognized as a climate action strategy to reduce terrestrial greenhouse gas emissions. Restoration efforts often require accurate inputs, like peat thickness (PT), for C-stock estimation and monitoring; however, these are often lacking or available at suboptimal accuracy levels. In this study, apparent electrical conductivity (ECa) from proximal electromagnetic induction (EMI) surveys and topographic variables derived from a LiDAR-based digital elevation model were assessed as covariates for PT mapping of an agricultural bog, separately and combined, using the quantile random forest algorithm. Local models were trained separately for the large (308 ha) and small (42 ha) EMI surveyed areas, while global models combined data from both areas for a full site analysis. The subsurface was characterized based on resistivity variations in inverted towed transient electromagnetic (tTEM) data. The results indicated that combining topographic and ECa covariates yielded the best PT prediction accuracy for the global model, with acoefficient of determination of 0.61 and a normalized root mean square error (NRMSE) of 0.10. The best large area local model was less accurate than the former (NRMSE of 0.18), while the best small area local model (NRMSE of 0.11) was superior to the best global model. Models trained with only topographic or ECa covariates were the least accurate, especially for the ECa-only model. The tTEM results revealed a heterogenous site characterized by a thin, resistive peat layer overlying stratified postglacial deposits of clay, sand, and saline chalk. Our findings show that covariates characterizing surface and subsurface properties are essential for accurate PT mapping and can inform tailored land use planning and restoration initiatives for degraded peatlands.

中文翻译：

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丹麦耕地沼泽的基于传感器的泥炭厚度测绘


抽干泥炭地用于农业，使它们成为重要的碳 （C） 来源。恢复干涸的泥炭地越来越被认为是减少陆地温室气体排放的气候行动策略。恢复工作通常需要准确的输入，例如泥炭厚度 （PT），用于 C 类库存估计和监测;然而，这些通常缺乏或以次优的精度水平获得。在这项研究中，来自近端电磁感应 （EMI） 调查的表观电导率 （ECa） 和来自基于 LiDAR 的数字高程模型得出的地形变量被评估为农业沼泽 PT 映射的协变量，分别和组合，使用分位数随机森林算法。本地模型分别针对大型 （308 公顷） 和小型 （42 公顷） EMI 调查区域进行训练，而全球模型则结合了来自两个区域的数据以进行完整的现场分析。根据倒置拖曳瞬态电磁 （tTEM） 数据中的电阻率变化对地下进行表征。结果表明，结合地形和 ECa 协变量为全局模型产生了最佳的 PT 预测精度，决定系数为 0.61，归一化均方根误差 （NRMSE） 为 0.10。最佳大面积局部模型的准确性不如前者 （NRMSE 为 0.18），而最好的小区域局部模型 （NRMSE 为 0.11） 优于最佳全局模型。仅使用地形或 ECa 协变量训练的模型最不准确，尤其是对于仅 ECa 的模型。tTEM 结果揭示了一个异质性场地，其特征是薄而抗性的泥炭层覆盖在粘土、沙子和盐水白垩的分层冰川后沉积物上。 我们的研究结果表明，表征表面和地下特性的协变量对于准确的 PT 绘图至关重要，并且可以为退化泥炭地的定制土地利用规划和恢复计划提供信息。