Forest soil is a large carbon (C) pool and plays a pivotal role in the global C cycle. The accurate estimation of soil organic carbon (SOC) stocks in forests is the cornerstone of studying the C budget; however, current assessments of forest SOC stocks are highly uncertain. One of the key reasons for this uncertainty is that most previous studies only used a few soil profiles for their estimation, whereas SOC stocks are highly spatially heterogeneous. To accurately evaluate the plot-level SOC stocks of China's forests, we conducted intensive soil sampling (100 soil cores within a plot) in 33 plots across 11 forest sites from south to north China. Moreover, to further reduce the cost of soil analysis, we applied the vis-NIR spectroscopy approach in SOC content estimation.
Results/Conclusions
The average SOC density (SOCD) of these forest sites was 137.4±12.1 Mg C ha-1 (0-100 cm), with significant geographic variations. We also showed that the error of the SOCD estimates obtained from the intensive soil sampling was significantly smaller than that of estimates obtained from the traditional sampling method (5.3±1.3% vs. 24.2±5.6%, with a confidence level of 0.95). Moreover, we found that the clustering by fast research and find of density peak in combination with the Cubist model showed an excellent spectroscopic prediction ability of SOC content (R2=0.96, RPIQ=5.83). Overall, this study suggest that intensive sampling can significantly reduce the uncertainty in forest SOC stock estimation by guarding against the effects of spatial heterogeneity, and proved the cost-efficiency of spectroscopic methodology in large-scale SOC estimation, and therefore provide an important methodological reference for accurately evaluating forest SOC stocks and C budgets in other regions.