Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models

Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models

The CO _2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO _2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrop...

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Bibliographic Details
Main Authors: Haibo Lu, Shihua Li, Minna Ma, Vladislav Bastrikov, Xiuzhi Chen, Philippe Ciais, Yongjiu Dai, Akihiko Ito, Weimin Ju, Sebastian Lienert, Danica Lombardozzi, Xingjie Lu, Fabienne Maignan, Mahdi Nakhavali, Timothy Quine, Andreas Schindlbacher, Jun Wang, Yingping Wang, David Wårlind, Shupeng Zhang, Wenping Yuan
Format: Article
Language:English
Published: IOP Publishing 2021-01-01
Series:Environmental Research Letters
Subjects:
benchmark
carbon cycling
global soil respiration
machine learning
terrestrial ecosystem models
Online Access:https://doi.org/10.1088/1748-9326/abf526

Internet

https://doi.org/10.1088/1748-9326/abf526

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