Re-Estimating GEDI Ground Elevation Using Deep Learning: Impacts on Canopy Height and Aboveground Biomass

This paper presents a method to improve ground elevation estimates through waveform analysis from the Global Ecosystem Dynamics Investigation (GEDI) and examines its impact on canopy height and aboveground biomass (AGB) estimation. The method uses a deep learning model to estimate ground elevation from the GEDI waveform. Geographic transferability was demonstrated by recalculating canopy height and AGB estimation accuracy using the improved ground elevation without changing established GEDI formulas for relative height (RH) and AGB. The study covers four regions in Japan and South America, from subarctic to tropical zones, integrating GEDI waveform data with airborne laser scan (ALS) data. Transfer learning was explored to enhance accuracy in regions not used for training. Ground elevation estimates using deep learning showed an RMSE improvement of over 3 m compared to the conventional GEDI L2A product, with generalization performance. Applying transfer learning and retraining with additional data further improved the estimation accuracy, even with limited datasets. The findings suggest that improving ground elevation estimates enhances canopy height and AGB accuracy, maximizing GEDI’s global AGB estimation algorithms. Optimizing models for each region could further enhance accuracy. The broader application of this method may improve global carbon cycle understanding and climate models.