Improving GEDI AGB Estimation in South Asian Tropical Forests - Towards Synergistic Calibration with NISAR

The recent availability of Above Ground Biomass (AGB) estimates from the Global Ecosystem Dynamics Investigation (GEDI) LiDAR system has enabled large-scale AGB estimation, particularly for calibrating other Earth Observation (EO) datasets. However, its accuracy over South Asian tropical forests remains uncertain due to limited regional calibration data. This study evaluates GEDI-derived AGB estimates against high-resolution Airborne LiDARderived AGB (ALS-AGB) maps across five tropical forest sites in South Asia. Results reveal significant discrepancies, with GEDI overestimating AGB for grasslands and shrubs ($\sim 38 \%$) while underestimating AGB for deciduous and evergreen forests ($\sim 35 \%$). Root mean square errors (RMSE) ranged from 40% to 64%, indicating substantial bias. Applying a first-order bias correction model to Evergreen Broadleaf Trees improved accuracy, reducing RMSE from 46% to 35% and bias from 34% to 6%. These findings highlight the necessity of region-specific calibration to enhance GEDI’s reliability for EO-based biomass mapping. The corrected GEDI AGB estimates offer a more accurate reference for upcoming missions like NISAR, contributing to improved global AGB assessments and forest carbon monitoring.