Stable gap-filling for longer eddy covariance data gaps: A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes

Songyan Zhu* (Corresponding Author), Robert Clement, Jon McCalmont, Christian A. Davies, Timothy Hill

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
6 Downloads (Pure)

Fingerprint

Dive into the research topics of 'Stable gap-filling for longer eddy covariance data gaps: A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes'. Together they form a unique fingerprint.

Earth & Environmental Sciences

Agriculture & Biology