Improving the Spatial Continuity of GEDI Aboveground Biomass Density Products Using Multisource Remote Sensing Data With Consideration of Spatial Correlation and Heterogeneity

Accurate, long-term estimation of forest aboveground biomass density (AGBD) is essential for monitoring terrestrial ecosystem dynamics and quantitatively assessing the capacity of forests in the global carbon cycle and their contribution to mitigating climate change. Meanwhile, plot-level AGBD measurements are highly accurate but lack spatial and temporal continuity. Global ecosystem dynamics investigation (GEDI) provides spatially discontinued estimates of global-level AGBD. To overcome these challenges, we proposed a model incorporating spatial correlation and heterogeneity for AGBD estimation, which combines GEDI gridded data with multisource remote sensing data. To further mitigate the spatial discontinuities in GEDI-derived AGBD distribution, a LightGBM model (LGB_EV_SIT) incorporating spatial eigenvectors (EV) and spatial interaction terms (SIT) was proposed. Using GEDI-derived AGBD as a reference, key predicted indicators were identified and ranked based on multisource remote sensing variables. A spatial weight matrix was constructed to reflect the spatial distribution of the GEDI AGBD grids. Spatial EVs and SITs were extracted based on the spatial weight matrix. Compared with the LightGBM model (LGB) without considering the correlation and interaction (r = 0.61–0.82, MRE = 38.6% –54.3%, RMSE = 45.91–54.60 Mg/ha), LGB_EV_SIT exhibits better performance (r = 0.85–0.87, MRE = 31.5% –39.1%, RMSE = 36.47–46.17 Mg/ha). Furthermore, the framework was applied to map forest AGBD across the western USA from 2019 to 2022, enabling the detection of forest changes over time. The proposed model provides new possibilities to accurately generate wall-to-wall maps of forest biomass and carbon stocks over a long time series.