Abstract Accurately quantifying canopy height and above-ground carbon across diverse land-cover types is crucial for understanding carbon storage dynamics and guiding climate-mitigation strategies. Yet existing maps often overlook non-forest ecosystems. Here we present a deep learning framework based on a U-Net architecture that combines radar, optical, elevation and slope data to produce a 10 m canopy height map across China. The model is trained with laser measurements from NASA’s GEDI mission validated using unmanned aerial vehicle lidar data ( MAE = 2.39 m ). We then estimate the above-ground biomass and carbon from these heights using a Random Forest model ( MAE = 37.71 Mg ha-1 ). By deriving carbon from canopy height, we take advantage of U-Net’s ability to capture trees in non-forest ecosystems such as croplands, grasslands and urban areas. Our nationwide 30 m carbon map reveals that trees outside forests contribute 20.8-32.9% of China’s above-ground carbon in 2019 (3.62-5.72 Pg C), underscoring their importance.
Su et al. (Thu,) studied this question.