Machine learning-based multi-scale dynamics of terrestrial carbon fluxes and their environmental drivers along the U.S. East Coast

Spatiotemporal Dynamics and Environmental Associations of Vegetation Carbon Sinks in the Middle and Lower Yellow River Basin, China

Vegetation carbon sinks are an important component of the terrestrial carbon cycle, and net ecosystem productivity (NEP) is widely used to indicate ecosystem carbon-sink strength. However, the long-term dynamics and environmental associations of vegetation carbon sinks in the middle and lower Yellow River Basin remain...

Spatial prediction and drivers of surface soil organic carbon density in subtropical montane forests

Soil organic carbon (SOC) represents the largest terrestrial carbon (C) pool, and even small changes in its stock can exert great influences on regional and global C cycles. Quantifying the spatial distribution of forest SOC and identifying its key drivers are therefore central to climate change science. In this study,...

[Current Status and Prospects of Machine Learning Applications in Terrestrial Ecosystem Carbon Sink Modeling].

Spatiotemporal Dynamics and Driving Factors Analysis of Vegetation Net Primary Productivity in China Using Remote Sensing and Machine Learning

Net primary productivity (NPP) is a key indicator of ecohydrological processes and vegetation responses—including in croplands—to hydrological and carbon dynamics under environmental change. However, existing studies rarely jointly analyze seasonal, interannual, and extreme-event NPP responses or account for spatial he...

[Prediction of Global Warming Potential of Qilian Mountain Grassland Ecosystem Based on Ensemble Machine Learning].

Evaluating modeling approaches for estimating net ecosystem exchange in Canadian peatlands

Peatlands are globally significant carbon reservoirs, yet models for peatland carbon cycling are often limited to the site level. To enable the prediction of carbon dioxide (CO 2 ) fluxes in peatlands where no in situ measurements exist, machine learning algorithms must be trained on in situ CO 2 flux measureme...