Spatial distribution and drivers of aboveground forest biomass in Mexico using GEDI and national forest inventory data

Accurate assessment of forest aboveground biomass density (AGBD) is essential for understanding the role of vegetation in climate change mitigation and developing forest management and environmental policies at national and regional levels. The Global Ecosystem Dynamics Investigation (GEDI) uses full‐waveform LiDAR and provides a valuable tool for estimating AGBD. Calibrating GEDI biomass products with local field data is vital for improving model accuracy, as current estimates rely on global datasets. Additionally, evaluating key factors that influence biomass estimation is essential to refine GEDI‐based models. In this research, we calibrated linear models with field AGBD as the dependent variable and GEDI metrics as independent variables, and compared the performance against the GEDI L4A product across forest types. Additionally, we evaluated the effects of terrain slope, forest structural complexity, and forest type on the accuracy of the models. Finally, we mapped AGBD in Mexico by aggregating footprint‐level estimates with local models and compared it with the GEDI AGBD map (L4B product). Model validation showed R2 values from 0.35 to 0.46 across forest types, with most models having %RMSE below 52.0. Errors were 32.7 to 34.2% lower than GEDI L4A, highlighting a notable accuracy improvement. The total carbon stocks in Mexico estimated here are approximately 1.78 Gt, aligning closely with official FAO estimates, whereas GEDI estimates are 33.5% higher than the official estimate. Biomass estimation with GEDI is most accurate in areas with moderate slopes and low forest structural complexity. Coniferous and tropical forests showed the lowest errors in estimating AGBD with GEDI (46.7 and 47.3 of %RMSE, respectively) likely due to the widespread presence of uniformly structured coniferous trees and the moderate terrain slopes found in tropical forests. Our findings highlight the importance of calibrating local AGBD data with GEDI forest structure metrics to improve biomass estimations at the footprint and national levels.