资源环境科技发展态势分析平台

Accurate quantification of vegetation carbon turnover time (τ_veg) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τ_veg could only be estimated based on net primary productivity under the steady‐state assumption. Here, we applied a machine‐learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation‐based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year⁻¹. By contrast, land‐surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τ_veg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation‐based τ_veg, modelled τ_veg tended to underestimate τ_veg at high latitudes. Our empirically derived gridded datasets of litter production and τ_veg will help constrain global vegetation models and improve the prediction of global carbon cycle.