Estimation of Forest Aboveground Biomass in China Based on GEDI and Sentinel-2 Data: Quantitative Analysis of Optical Remote Sensing Saturation Effect and Terrain Compensation Mechanisms

Abstract

Highlights: What are the main findings? This study generated a high-resolution (200 m) forest aboveground biomass (AGB) map of China and provides a national-scale quantitative analysis of optical saturation thresholds. Single-band reflectance saturates at ~80 Mg·ha−1, while certain spectral indices used in this study (NDSIs) delay saturation to 100–150 Mg·ha−1. It systematically uncovers the compensatory role of topographic factors. Terrain features like slope and elevation variability stably support AGB prediction in medium-to-high biomass regions (<300 Mg·ha−1), mitigating the impact of optical saturation. What is the implication of the main finding? The proposed analytical framework offers a physically interpretable and transferable approach for quantifying saturation and compensation mechanisms, guiding integration of multi-source data (e.g., radar/SAR) to overcome optical saturation. This work clearly delineates the potential and limitations of optical imagery, providing methodological guidance for large-scale, long-term biomass monitoring and historical AGB reconstruction. Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha−1. Red and red-edge bands saturated at around 80 Mg·ha−1, while certain spectral indices delayed the threshold to 100–150 Mg·ha−1. Topographic features maintained stable contributions below 300 Mg·ha−1, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring.