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...

Volledige beschrijving

Bewaard in:
Bibliografische gegevens
Hoofdauteurs: 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
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Frontiers Media S.A. 2020-06-01
Reeks:Frontiers in Big Data
Onderwerpen:
soil organic carbon representation
Earth system model
data assimilation
deep learning
Community Land Model version 5 (CLM5)
soil carbon dynamics
Online toegang:https://www.frontiersin.org/article/10.3389/fdata.2020.00017/full
Tags: Voeg label toe
Geen labels, Wees de eerste die dit record labelt!

Gelijkaardige items