Peatlands store a large amount of carbon. However, peatlands are complex ecosystems, and acquiring reliable estimates of how much carbon is stored underneath the Earth’s surface is inherently challenging, even at small scales. Here, we aim to establish links between the above- and below-ground factors that control soil carbon status, identify the key environmental variables associated with carbon storage, as well as to explore the potential for using Unmanned Aerial Vehicle (UAV) remote sensing for spatial mapping of peatlands. We combine UAVs equipped with Red-Green-Blue (RGB), multispectral, thermal infrared, and light detection and ranging (LiDAR) sensors with ground-penetrating radar (GPR) technology and traditional field surveys to provide a comprehensive, 3-dimensional mapping of a peatland hillslope-floodplain landscape in the Belgian Hautes Fagnes. Our results indicate that both peat thickness and soil organic carbon (SOC) stock (top 1 m) are spatially heterogeneous and that the contributions from the surface topography to peat thickness and SOC stock varied from micro- to macro-scales. Peat thickness was more strongly controlled by macro-topography (R2 = 0.46) than SOC stock, which was more influenced by micro-topography (R2 = 0.21). Current vegetation had little predictive power for explaining their spatial variability. Additionally, the UAV data provided accurate estimates of both peat thickness and SOC stock, with RMSE and R2 values of 0.16 m and 0.85 for the peat thickness, and 59.25 t/ha and 0.85 for the SOC stock. However, similar performance can already be achieved by using only topographical data from the LiDAR sensor (for peat thickness) and a combination of peat thickness and topography (for SOC stock) as predictor variables. Our study bridges the gap between surface observations and the hidden carbon reservoir below. This not only allows us to improve our ability to assess the spatial distribution of SOC stocks, but also contributes to our understanding of the environmental factors associated with SOC storage in these highly heterogeneous landscapes, providing insights for environmental science and climate projections.

中文翻译：

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基于无人机高分辨率遥感的温带泥炭地泥炭土厚度和碳储量控制因素研究


泥炭地储存了大量的碳。然而，泥炭地是复杂的生态系统，即使在小规模下，也要可靠地估计地球表面下储存了多少碳，这本身就具有挑战性。在这里，我们旨在建立控制土壤碳状况的地上和地下因素之间的联系，确定与碳储存相关的关键环境变量，并探索使用无人机 （UAV） 遥感进行泥炭地空间测绘的潜力。我们将配备红-绿-蓝 （RGB）、多光谱、热红外和光探测和测距 （LiDAR） 传感器的无人机与探地雷达 （GPR） 技术和传统实地调查相结合，提供比利时 Hautes Fagnes 泥炭地山坡-洪泛区景观的全面三维地图。我们的结果表明，泥炭厚度和土壤有机碳 （SOC） 储量（顶部 1 m）在空间上都是异质的，并且地表地形对泥炭厚度和 SOC 储量的贡献从微观到宏观都不同。泥炭厚度受宏观地形 （R2 = 0.46） 的控制比 SOC 储量更强，而 SOC 储量受微观地形 （R2 = 0.21） 的影响更大。当前的植被对解释其空间变异性的预测能力很小。此外，无人机数据提供了泥炭厚度和 SOC 存量的准确估计值，泥炭厚度的 RMSE 和 R2 值为 0.16 m 和 0.85，SOC 存量为 59.25 t/ha 和 0.85。然而，仅使用来自 LiDAR 传感器的地形数据（用于泥炭厚度）和泥炭厚度和地形（用于 SOC 库存）的组合作为预测变量，已经可以实现类似的性能。 我们的研究弥合了地表观测与下面隐藏的碳储层之间的差距。这不仅使我们能够提高评估 SOC 储量空间分布的能力，还有助于我们了解这些高度异质景观中与 SOC 储存相关的环境因素，为环境科学和气候预测提供见解。