Research data-Hot spots, hot moments, and spatiotemporal drivers of soil CO2 flux in temperate peatlands using UAV remote sensing

CO2 emissions from peatlands exhibit substantial spatiotemporal variability, presenting challenges for identifying the underlying drivers and for accurately quantifying and modeling CO2 fluxes. Here, we integrated field measurements with Unmanned Aerial Vehicle (UAV)-based multi-sensor remote sensing to investigate soil respiration across a temperate peatland landscape. Our research addressed two key questions: (1) How do environmental factors control the spatiotemporal distribution of soil respiration across complex landscapes? (2) How do spatial and temporal peaks (i.e., hot spots and hot moments) of biogeochemical processes influence landscape-level CO2 fluxes? We find that dynamic variables (i.e., soil temperature and moisture) play significant roles in shaping CO2 flux variations, contributing 43 % to seasonal variability and 29 % to spatial variance, followed by semi-dynamic variables (i.e., Normalized Difference Vegetation Index (NDVI) and root biomass) (19 % and 24 %). Relatively static variables (i.e., soil organic carbon stock and carbon to nitrogen ratio) have a minimal influence on seasonal variation (2 %) but contribute more to spatial variance (10 %). Additionally, predicting time series of CO2 fluxes is feasible by using key environmental variables (test set: coefficient of determination (R2) = 0.74, Root Mean Square Error (RMSE) = 0.57 μmol m⁻² s⁻¹, Kling-Gupta Efficiency (KGE) = 0.77), while UAV remote sensing is an effective tool for mapping daily soil respiration (test set: R2 = 0.75, RMSE = 0.56 μmol m⁻² s⁻¹, KGE = 0.83). By the integration of in-situ high-resolution time-lapse monitoring and spatial mapping, we find that despite occurring in 10 % of the year, hot moments (i.e., periods of time which have a disproportional high CO2 fluxes compared to the surrounding) contribute 28 %–31 % of the annual CO₂ fluxes. Meanwhile, hot spots (i.e., locations which have a high CO2 fluxes)—representing 10 % of the area—account for about 20 % of CO₂ fluxes across the landscape. Our study demonstrates that integrating UAV-based remote sensing with field surveys improves the understanding of soil respiration mechanisms across timescales in complex landscapes. This will provide insights into carbon dynamics and supporting peatland conservation and climate change mitigation efforts.

泥炭地的二氧化碳（CO₂）排放具有显著的时空变异性，这为识别其潜在驱动因子、精准量化与模拟二氧化碳通量带来了挑战。本研究将野外实测数据与无人机（Unmanned Aerial Vehicle, UAV）搭载的多传感器遥感技术相结合，针对温带泥炭地景观的土壤呼吸展开研究。本研究聚焦两个核心科学问题：(1) 环境因子如何调控复杂景观中土壤呼吸的时空分布？(2) 生物地球化学过程的时空峰值（即热点区域（hot spots）与热点时段（hot moments））如何影响景观尺度的二氧化碳通量？研究发现，动态变量（如土壤温度与湿度）对二氧化碳通量变化的塑造作用显著，其对季节变异性的贡献达43%，对空间异质性的贡献达29%；其次为半动态变量（如归一化植被指数（Normalized Difference Vegetation Index, NDVI）与根系生物量），贡献分别为19%与24%。相对静态的变量（如土壤有机碳储量与碳氮比）对季节变异的影响极小（仅2%），但对空间异质性的贡献更高（10%）。此外，利用关键环境变量即可实现二氧化碳通量的时间序列预测（测试集：决定系数（coefficient of determination, R²）=0.74，均方根误差（Root Mean Square Error, RMSE）=0.57 μmol·m⁻²·s⁻¹，克林-古普塔效率（Kling-Gupta Efficiency, KGE）=0.77）；而无人机遥感则可有效实现逐日土壤呼吸的空间制图（测试集：R²=0.75，RMSE=0.56 μmol·m⁻²·s⁻¹，KGE=0.83）。通过整合原位高分辨率时序监测与空间制图，本研究发现，尽管热点时段仅占全年时长的10%，但其贡献了28%~31%的年度二氧化碳通量；与此同时，占研究区总面积10%的热点区域，贡献了景观尺度约20%的二氧化碳通量。本研究表明，将无人机遥感与野外调查相结合，可提升对复杂景观中不同时间尺度下土壤呼吸机制的认知。该研究成果可为碳动态研究提供新视角，并为泥炭地保护与气候变化减缓行动提供支撑。