Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning

The dynamics of soil organic carbon (SOC) play a critical role in
modulating global warming. However, the long-term spatiotemporal changes
of SOC at large scale and the impacts of driving forces remain unclear. In
this study, we investigated the dynamics of SOC in different soil layers
across China through the 1980s to 2010s using a machine learning approach
and quantified the impacts of the key factors based on factorial
simulation experiments. Our results showed that the latest (2000-2014) SOC
stock in the first meter soil (SOC100) was 80.68 ± 3.49 Pg C, of which
42.6% was stored in the top 20 cm, sequestrating carbon with a rate of
30.80 ± 12.37 g C m-2 yr-1 since the 1980s. Our experiments focusing on
the recent two periods (2000s and 2010s) revealed that climate change
exerted the largest relative contributions to SOC dynamics in both layers
and warming or drying can result in SOC loss. However, the influence of
climate change weakened with soil depth, while the opposite for vegetation
growth. Relationships between SOC and forest canopy height further
confirmed this strengthened impact of vegetation with soil depth, and
highlighted the carbon sink function of deep soil in mature forest.
Moreover, our estimates suggested that SOC dynamics in 71% of topsoil were
controlled by climate change and its coupled influence with environmental
variation (CE). Meanwhile CE and the combined influence of climate change
and vegetation growth dominated the SOC dynamics in 82.05% of the first
meter soil. Additionally, the national cropland topsoil organic carbon
increased with a rate of 23.6 ± 7.6 g C m-2 yr-1 since the 1980s, and the
widely applied nitrogenous fertilizer was a key stimulus. Overall, our
study extended the knowledge about the dynamics of SOC and deepened our
understanding about the impacts of the primary factors.