Explaining ecological drivers and management implications of forest biomass: An explainable deep learning fusion of remote sensing and climate data

• Multi-source deep learning framework improves forest biomass estimation. • Integration of Climate-FVS data enhances accuracy in complex terrains. • Explainable AI reveals key ecological drivers of aboveground biomass. • SWIR and Red bands identified as the most influential spectral predictors. • Findings support carbon accounting and sustainable forest management. Accurate estimation of aboveground biomass (AGB) is essential for sustainable forest management and climate change monitoring; however, conventional remote sensing approaches often rely on a single data type and provide limited interpretability. This study introduces a novel framework that systematically integrates multi-sensor remote sensing (Sentinel-1 SAR, Sentinel-2, and Landsat-8 optical data), process-based climate variables (Climate-FVS), and LiDAR-derived topographic indices into deep learning models. By coupling these fusion scenarios with explainable AI (XAI) analyses, the framework not only improves predictive accuracy but also reveals the relative contribution of ecological drivers such as precipitation, temperature, and short-wave infrared bands. Seven U-Net-based architectures, including U-Net3+, TransU-Net, and Attention U-Net, were trained and evaluated using RMSE, MAE, and R² metrics. Results showed that U-Net3+ achieved the highest performance under the complete fusion scenario, with an RMSE of 28.10 Mg/ha, an MAE of 17.49 Mg/ha, and an R² of 0.89. XAI analyses highlighted that SWIR1, SWIR2, and Red bands were the most influential predictors, while climatic variables significantly improved model generalization in topographically complex areas. The highest errors occurred at vegetation boundaries and steep terrain. These findings demonstrate that multi-source data fusion combined with interpretable deep learning provides a robust pathway for both accurate AGB estimation and a deeper understanding of its environmental drivers, directly supporting carbon accounting and sustainable forest management.