气候变化领域集成服务门户(CCPortal): Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling

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DOI	10.1016/j.rse.2019.111584
	Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling
	Li L.; Franklin M.; Girguis M.; Lurmann F.; Wu J.; Pavlovic N.; Breton C.; Gilliland F.; Habre R.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78–0.81; mean RMSE = 0.013–0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications. © 2019 Elsevier Inc.
英文关键词	Aerosol Optical Depth; Air quality; Deep learning; Downscaling; MAIAC; MERRA-2 GMI Replay Simulation; Missingness imputation
语种	英语
scopus关键词	Aerosols; Air quality; Deep learning; Optical properties; Statistical methods; Aerosol optical depths; Down-scaling; MAIAC; MERRA-2 GMI Replay Simulation; Missingness imputation; Deep neural networks; AERONET; aerosol; air quality; algorithm; downscaling; instrumentation; model; optical depth; simulation; spatiotemporal analysis; surface reflectance; California; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179527
作者单位	Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China; Sonoma Technology, Inc., Petaluma, CA, United States; Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, United States
推荐引用方式 GB/T 7714	Li L.,Franklin M.,Girguis M.,et al. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling[J],2020,237.
APA	Li L..,Franklin M..,Girguis M..,Lurmann F..,Wu J..,...&Habre R..(2020).Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling.Remote Sensing of Environment,237.
MLA	Li L.,et al."Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling".Remote Sensing of Environment 237(2020).