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A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level 期刊论文
GLOBAL CHANGE BIOLOGY, 2019
作者:  Jiang, Hao;  Hu, Hao;  Zhong, Renhai;  Xu, Jinfan;  Xu, Jialu;  Huang, Jingfeng;  Wang, Shaowen;  Ying, Yibin;  Lin, Tao
收藏  |  浏览/下载：9/0  |  提交时间：2020/02/17
climate change impact  corn yield  deep learning  geospatial discovery  phenology  
Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis 期刊论文
GLOBAL CHANGE BIOLOGY, 2019
作者:  Kim, Yeonuk;  Johnson, Mark S.;  Knox, Sara H.;  Black, T. Andrew;  Dalmagro, Higo J.;  Kang, Minseok;  Kim, Joon;  Baldocchi, Dennis
收藏  |  浏览/下载：15/0  |  提交时间：2019/11/27
artificial neural network  comparison of gap-filling techniques  eddy covariance  machine learning  marginal distribution sampling  methane flux  random forest  support vector machine  
Anticipating global terrestrial ecosystem state change using FLUXNET 期刊论文
GLOBAL CHANGE BIOLOGY, 2019, 25 (7) : 2352-2367
作者:  Yu, Rong;  Ruddell, Benjamin L.;  Kang, Minseok;  Kim, Joon;  Childers, Dan
收藏  |  浏览/下载：10/0  |  提交时间：2019/11/27
eddy covariance  FLUXNET  functional elasticity  information flow  phenology  precipitation  process network  radiation  structural state  temperature