Multi-Source Spaceborne LiDAR Forest Canopy Height Retrieval by Integrating GEDI and ICESat-2

Abstract

Forest canopy height is critical for quantifying terrestrial carbon stocks, assessing ecosystem productivity, and supporting biogeochemical modeling. Spaceborne LiDAR missions (e.g., GEDI and ICESat-2) enable forest canopy height mapping from regional to global scales, but they differ substantially in spatial coverage and observation mechanisms, and their retrievals are subject to systematic biases that vary with complex environmental conditions. Using airborne LiDAR-derived canopy heights as the reference, we validated the performance of spaceborne LiDAR canopy height retrievals and analyzed the spatial distribution of retrieval residuals across environmental factors. We then constructed an XGBoost-based multi-source data fusion correction model for canopy height that accounts for the differential effects of environmental factors. Using Genhe as the study area, we found that spaceborne LiDAR-derived forest canopy heights are systematically underestimated before correction (GEDI: −2.17 m; ICESat-2: −2.41 m), with biases varying markedly across environmental factors. After correction, biases drop to 0.00 m and +0.02 m, respectively. The multi-source fusion model achieves an RMSE of 2.58 m and a correlation coefficient of 0.766 against airborne LiDAR references, significantly outperforming single-source corrected results. These findings verify the effectiveness of the proposed multi-source correction framework in heterogeneous environments.