A machine learning gap-filling approach for daily forest soil CO2 flux based on environmental factors and eddy covariance variables

Key Points

• Daily forest soil CO2 flux is accurately predicted using a machine learning approach, enhancing data reliability.
• Key metrics indicate up to 50% improvement in CO2 flux estimates over traditional methods.
• Analysis employed machine learning to integrate environmental factors and eddy covariance variables for better predictions.
• Highlights the importance of advanced modeling in capturing environmental dynamics affecting CO2 emissions.