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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：16/0  |  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  
Biochar application as a tool to decrease soil nitrogen losses (NH3 volatilization, N2O emissions, and N leaching) from croplands: Options and mitigation strength in a global perspective 期刊论文
GLOBAL CHANGE BIOLOGY, 2019, 25 (6) : 2077-2093
作者:  Liu, Qi;  Liu, Benjuan;  Zhang, Yanhui;  Hu, Tianlong;  Lin, Zhibin;  Liu, Gang;  Wang, Xiaojie;  Ma, Jing;  Wang, Hui;  Jin, Haiyang;  Ambus, Per;  Amonette, James E.;  Xie, Zubin
收藏  |  浏览/下载：8/0  |  提交时间：2019/11/26
biochar  machine learning  N leaching  N2O  NH3  spatial variability