This study proposes a method to improve the accuracy of ground elevation estimations by analyzing Global Ecosystem Dynamics Investigation (GEDI) waveforms. The impact on this new method on canopy-height and above-ground biomass (AGB) estimations was also evaluated. The method uses a deep learning model to derive ground elevations from GEDI waveform data. Geographic transferability was assessed by recalculating the accuracy of canopy-height and AGB estimates using the improved ground height while retaining the original GEDI formulas for relative height and AGB. GEDI waveform data with airborne laser scan (ALS) data from three regions in Japan were integrated, and transfer learning was applied to enhance the estimation accuracy in regions excluded from the training dataset. The results demonstrated that the deep learning-based ground elevation estimation both reduced the root mean squared error (RMSE) by >3 m compared to the previous GEDI L2A product and exhibited generalization performance. Further accuracy improvements were achieved through transfer learning and retraining with additional data, even with limited datasets. These findings demonstrate that improving the accuracy of ground elevation estimations can significantly optimize the GEDI global AGB estimation algorithm, achieving a canopy-height accuracy of approx. 1.5 m and AGB accuracy of approx. 70 t/ha. The method proposed herein is expected to improve the accuracy of GEDI ground elevation and AGB estimations outside of the study area, and it will be used for the Japanese space lidar mission MOLI scheduled for launch in 2027. The new method is expected to contribute to global and long-term high-precision AGB observations.
