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Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling 期刊论文
REMOTE SENSING OF ENVIRONMENT, 2020, 卷号: 237, 页码: 17
Authors:  Li, Lianfa;  Franklin, Meredith;  Girguis, Mariam;  Lurmann, Frederick;  Wu, Jun;  Pavlovic, Nathan;  Breton, Carrie;  Gilliland, Frank;  Habre, Rima
Favorite  |  View/Download:6/0  |  Submit date:2020/05/19
Aerosol Optical Depth  MAIAC  MERRA-2 GMI Replay Simulation  Deep learning  Downscaling  Missingness imputation  Air quality  
A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 2, 页码: 27
Authors:  Li, Lianfa
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PM2.5  satellite AOD  deep learning  autoencoder  residual network  exposure estimation  high spatiotemporal resolution  
Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model 期刊论文
ENVIRONMENTAL POLLUTION, 2018, 卷号: 243, 页码: 501-509
Authors:  Li, Runkui;  Ma, Tianxiao;  Xu, Qun;  Song, Xianfeng
Favorite  |  View/Download:19/0  |  Submit date:2019/05/23
Spatial pattern  Fine particulate matter  Land use regression model  MAIAC AOD  Beijing  
Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth 期刊论文
REMOTE SENSING OF ENVIRONMENT, 2018, 卷号: 217, 页码: 573-586
Authors:  Li, Lianfa;  Zhang, Jiehao;  Meng, Xia;  Fang, Ying;  Ge, Yong;  Wang, Jinfeng;  Wang, Chengyi;  Wu, Jun;  Kan, Haidong
Favorite  |  View/Download:21/0  |  Submit date:2019/05/23
PM2.5  MAIAC AOD  High spatiotemporal resolution  Temporal variation  AOD-PM2.5 associations  Spatial effects  Missingness  Machine learning  
PM2.5浓度的多源时空预测——以山东省为例 学位论文
硕士, 北京: 中国科学院研究生院, 2017
Authors:  张杰昊
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