发表机构
UMR TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Univ Montpellier; LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay; Université de Lorraine; Géodata Paris, IGN, LIF; Université Gustave Eiffel; School of GeoSciences, University of Edinburgh; National Centre for Earth Observation, UK; Office National des Forêts RDI（TETIS联合研究单位，法国国家农业食品与环境研究院，巴黎农学院，CIRAD，法国国家科研中心，蒙彼利埃大学; 气候与环境科学实验室/地球系统研究所，法国原子能和替代能源委员会，法国国家科研中心-凡尔赛大学，巴黎萨克雷大学; 洛林大学; Géodata Paris，法国国家地理信息研究所，LIF; 古斯塔夫·埃菲尔大学; 爱丁堡大学地球科学学院; 英国国家地球观测中心; 国家林业局研发部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对欧洲温带森林优化GEDI模拟器配置，通过比较约9500对观测与模拟RH剖面，发现采用强度加权和a3算法可将偏差降至0.44米和0.40米，并扩展可用数据范围。
AI 中文摘要
准确估算地上生物量密度对于量化森林碳储量至关重要。NASA的GEDI任务提供了宝贵的冠层结构数据，但其稀疏采样 necessitates 使用模拟器在实地清查地点校准生物量模型。Hancock等人（2019）广泛使用的模拟器从机载LiDAR点云模拟GEDI波形，但该模拟器从未在欧洲温带森林中得到验证。在此，我们利用国家机载LiDAR计划作为输入，比较了法国森林中约9,500对观测和模拟的GEDI相对高度（RH）剖面。我们区分了两个误差来源：波形建模差异，通过将模拟和真实RH指标均参照共同的ALS-derived地面高程来评估；以及地面探测偏差，通过将每个GEDI L2A处理算法与ALS参考进行比较来评估。在基线配置下，整个RH剖面的平均绝对偏差在叶盛期达到0.69米，在落叶期达到1.28米。切换到基于强度的回波加权并选择a3 L2A算法后，这些偏差分别降至0.44米和0.40米。a3算法还实现了近乎无偏的地面探测（-0.03米，而默认算法为-0.88米），直接减少了先前被忽视的误差来源。我们还表明，落叶期采集和低灵敏度射击（通常被排除在标准生物量产品之外）的模拟与对应数据同样可靠，大大扩展了潜在的校准和推断数据集。
英文摘要
Accurate estimation of aboveground biomass density is essential for quantifying forest carbon stocks. NASA's GEDI mission provides valuable canopy structure data, but its sparse sampling necessitates the use of simulators to calibrate biomass models at field inventory locations. The widely used simulator of Hancock et al. (2019) emulates GEDI waveforms from airborne LiDAR point clouds, yet it has never been validated over European temperate forests. Here, we compare approximately 9,500 pairs of observed and simulated GEDI relative height (RH) profiles across French forests using the national airborne LiDAR program as input. We separate two sources of error: waveform modeling differences, assessed by referencing both simulated and real RH metrics to a common ALS-derived ground elevation, and ground detection bias, evaluated by comparing each GEDI L2A processing algorithm against the ALS reference. Under the baseline configuration, the mean absolute bias across the full RH profile reaches 0.69 m in leaf-on and 1.28 m in leaf-off conditions. Switching to intensity-based return weighting and selecting the a3 L2A algorithm reduces these biases to 0.44 m and 0.40 m respectively. The a3 algorithm also achieves near-unbiased ground detection (-0.03 m versus -0.88 m for the default), directly reducing a previously overlooked source of error. We also show that leaf-off acquisitions and low-sensitivity shots, both typically excluded from standard biomass products, are simulated as reliably as their counterparts, substantially expanding the potential calibration and inference datasets.
CommentsSubmitted to AGU Earth and Space Science (ESS). Presented at EGU General Assembly 2026, Vienna, Austria, abstract EGU26-9762. DOI: https://doi.org/10.5194/egusphere-egu26-9762
