Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale

Water environments (e.g., oceans, lakes, and rivers) are important carbon sinks and sources and contribute to the carbon cycle of the earth's ecosystem. Machine learning provides a potential solution for recognizing greenhouse gas (GHG) emissions from water based on big data analysis. Data-driven machine learning can comprehensively recognize the key environmental drivers that affect GHG emissions. However, several urgent issues should be addressed to guarantee machine learning recognition of GHG emissions from water. For example, matching in situ data and databases is the greatest challenge in conducting large-scale machine learning research. It is imperative to unify in situ data collection methods and improve database quality (e.g., spatiotemporal matching and high resolution). Quantifying the contributions of human activity and climate change to GHG emissions from water is urgently needed to resolve future challenges. Beyond providing recognition and prediction, machine learning, due to its interpretability, can optimize the carbon cycle model; thus, empirical formulas for machine learning deserve attention. Overall, machine learning can manage large-scale and complicated data for climate change regarding GHG emissions from water.