Optimizing GEDI Simulator Configuration for European Temperate Forests
Abstract
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.
Related
- Bridging forest inventory gaps in remote and inaccessible temperate mountain forests using AlphaEarth embeddings and model-based inference
- Allometric scaling relationships of biomass for six common understory species in French temperate forests
- Discrepancies between Sentinel-2 and LiDAR-derived LAI in temperate deciduous forests: Influence of apparent optical depth, radiative transfer priors, and horizontal heterogeneity
- Alarming decline in the carbon sink of European forests driven by disturbances
- Functional traits and acclimation of Central European deciduous forests during the Piacenzian
BibTeX
@misc{behloul2026optimizing,
title = {{Optimizing GEDI Simulator Configuration for European Temperate Forests}},
author = {Selim Behloul and Nikola Besic and Steven Hancock and Cedric Vega and Sylvie Durrieu and Jean-Pierre Renaud and Ibrahim Fayad and Philippe Ciais},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11440},
eprint = {2609.11440},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11440},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Optimizing GEDI Simulator Configuration for European Temperate Forests
AU - Selim Behloul
AU - Nikola Besic
AU - Steven Hancock
AU - Cedric Vega
AU - Sylvie Durrieu
AU - Jean-Pierre Renaud
AU - Ibrahim Fayad
AU - Philippe Ciais
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.11440
UR - https://arxiv.org/abs/2609.11440
AB - 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.
ER -