Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States

Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States

Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and...

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Bibliographic Details
Main Authors: Feng Tao, Zhenghu Zhou, Yuanyuan Huang, Qianyu Li, Xingjie Lu, Shuang Ma, Xiaomeng Huang, Yishuang Liang, Gustaf Hugelius, Lifen Jiang, Russell Doughty, Zhehao Ren, Yiqi Luo
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Big Data
Subjects:
soil organic carbon representation
Earth system model
data assimilation
deep learning
Community Land Model version 5 (CLM5)
soil carbon dynamics
Online Access:https://www.frontiersin.org/article/10.3389/fdata.2020.00017/full

Internet

https://www.frontiersin.org/article/10.3389/fdata.2020.00017/full

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