Forest aboveground biomass retrieval integrating ICESat-2, Landsat-8, and environmental factors

The synergistic integration of optical imSagery and LiDAR data provides a comprehensive spatial framework for the precise estimation of aboveground biomass (AGB). However, the technical pathway for AGB estimation in complex mountainous regions using multi-source heterogeneous data, including active and passive remote sensing and environmental data, requires further validation. This study proposes a novel framework for high-resolution AGB retrieval by integrating ICESat-2 LiDAR and Landsat-8 data, along with meteorological and topographic factors. AGB estimates were derived from ICESat-2 footprints using second-class forest survey data from the Jinsha River Basin, China. Relationships between canopy metrics and AGB were analyzed across beam types using LASSO and random forest (RF) models. The optimized RF model was then used to generate wall-to-wall AGB maps incorporating Landsat-8, meteorological, and topographic variables. The Nighttime-Strong beam achieved the highest AGB retrieval accuracy (R² = 0.71), followed by the Nighttime-Weak beam (R² = 0.69), all beams combined (R² = 0.68), the Daytime-Strong beam (R² = 0.68), and the Daytime-Weak beam (R² = 0.55); the LASSO model outperformed the RF model. In the AGB retrieval model using canopy metrics, mean canopy height, relative canopy height, canopy coverage, and canopy quadratic mean were strong predictors (correlation coefficients of 0.67, 0.65, 0.63, and 0.62, respectively). Adding meteorological and topographic data substantially improved wall-to-wall AGB mapping, with topography having a greater impact than meteorology. In conclusion, AGB retrieval accuracy can be markedly improved by using ICESat-2 Nighttime-Strong beams combined with meteorological and topographic datasets. This study proposes a more precise and effective methodology for forest monitoring in complex environments.