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Title: MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations
Abstract

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, calledMetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

 
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NSF-PAR ID:
10430996
Author(s) / Creator(s):
; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
Scientific Data
Volume:
10
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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