NASA Contract, United States [#NNL15AA03C] for the development and execution of the GEDI mission.FCT – Fundação para a Ciência e Tecnologia, I.P. by project reference UID/00239: Forest Research Centre, and LA/P/0092/2020 of Associate Laboratory TERRA.Foundation for Science and Technology (FCT), Portugal to Dr. Guerra- Hernández (#CEECIND/02576/2022).Pascual, AdriánMay, Paul BCárdenas Martínez, AarónGuerra Hernández, JuanHunka, NehaBruening, Jamis M.Healey, Sean P.Armston, John D.Dubayah, Ralph O.2025-04-012025-04-012025-02Pascual, A., May, P.B., Cárdenas Martínez, A., Guerra Hernández, J., Hunka, N., Bruening, J.M.,...,Dubayah, R.O. (2025). Calibration of GEDI footprint aboveground biomass models in Mediterranean forests with NFI plots: A comparison of approaches. Journal of Environmental Management, 375, 1-12. https://doi.org/10.1016/j.jenvman.2025.124313.1095-86300301-4797https://hdl.handle.net/11441/171229Observations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in the GEDI mission are based on training data sourced from sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) are rarely used to calibrate GEDI footprint biomass models because their sampling and positional accuracy prevent accurate colocation with GEDI or ALS. This mission can limit the harmonization of jurisdictional biomass estimates from NFI’s and GEDI; however, there are methods available to improve the colocation of NFI plots with GEDI footprints. Focusing on Mediterranean forests in Spain, we compared different approaches to the collocation of NFI and GEDI data: (i) simulated waveforms from ALS; (ii) nearest-neighbor on-orbit GEDI waveforms; and (iii) imputed GEDI waveforms imputed to NFI plot locations using a novel geostatistical method. These methods are potential solutions to improve the local performance of biomass models and address potential local systematic deviations between GEDI and NFI estimates. We assess the advantages and limitations of these methods to locally calibrate GEDI biomass models and quantify the impact of geolocation errors in reference NFI plot data. The new biomass models from each method were used to predict footprint level AGBD, which were then gridded for a province in the North-West of Spain. It was found that the imputation approach is not sensitive to common errors in NFI plot geolocation, but it can outperform ALS-based simulation in some cases, highlighting the benefit of information from multiple GEDI footprints proximate to NFI plots for improving biomass predictions. This research provides users with benchmark of available techniques to locally-calibrate GEDI footprint biomass models.application/pdf12engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Spaceborne lidar GEDI calibration Biomass modelling Inventory plotsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1016/j.jenvman.2025.124313