Cite
Two for one: Partitioning CO2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning.
MLA
Zhan, Weiwei, et al. “Two for One: Partitioning CO2 Fluxes and Understanding the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity Using Machine Learning.” Agricultural & Forest Meteorology, vol. 321, June 2022, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.agrformet.2022.108980.
APA
Zhan, W., Yang, X., Ryu, Y., Dechant, B., Huang, Y., Goulas, Y., Kang, M., & Gentine, P. (2022). Two for one: Partitioning CO2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning. Agricultural & Forest Meteorology, 321, N.PAG. https://doi.org/10.1016/j.agrformet.2022.108980
Chicago
Zhan, Weiwei, Xi Yang, Youngryel Ryu, Benjamin Dechant, Yu Huang, Yves Goulas, Minseok Kang, and Pierre Gentine. 2022. “Two for One: Partitioning CO2 Fluxes and Understanding the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity Using Machine Learning.” Agricultural & Forest Meteorology 321 (June): N.PAG. doi:10.1016/j.agrformet.2022.108980.