Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling
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 characteriz...
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ftpubmed:oai:pubmedcentral.nih.gov:7063693 2023-05-15T13:06:43+02:00 Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling Li, Lianfa Franklin, Meredith Girguis, Mariam Lurmann, Frederick Wu, Jun Pavlovic, Nathan Breton, Carrie Gilliland, Frank Habre, Rima 2019-12-10 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063693/ http://www.ncbi.nlm.nih.gov/pubmed/32158056 https://doi.org/10.1016/j.rse.2019.111584 en eng http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063693/ http://www.ncbi.nlm.nih.gov/pubmed/32158056 http://dx.doi.org/10.1016/j.rse.2019.111584 Remote Sens Environ Article Text 2019 ftpubmed https://doi.org/10.1016/j.rse.2019.111584 2021-02-07T01:27:27Z 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 R(2) = 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 R(2) = 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; R(2) = 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. Text Aerosol Robotic Network PubMed Central (PMC) Merra ENVELOPE(12.615,12.615,65.816,65.816) Remote Sensing of Environment 237 111584 |
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Article Li, Lianfa Franklin, Meredith Girguis, Mariam Lurmann, Frederick Wu, Jun Pavlovic, Nathan Breton, Carrie Gilliland, Frank Habre, Rima Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling |
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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 R(2) = 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 R(2) = 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; R(2) = 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. |
format |
Text |
author |
Li, Lianfa Franklin, Meredith Girguis, Mariam Lurmann, Frederick Wu, Jun Pavlovic, Nathan Breton, Carrie Gilliland, Frank Habre, Rima |
author_facet |
Li, Lianfa Franklin, Meredith Girguis, Mariam Lurmann, Frederick Wu, Jun Pavlovic, Nathan Breton, Carrie Gilliland, Frank Habre, Rima |
author_sort |
Li, Lianfa |
title |
Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling |
title_short |
Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling |
title_full |
Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling |
title_fullStr |
Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling |
title_full_unstemmed |
Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling |
title_sort |
spatiotemporal imputation of maiac aod using deep learning with downscaling |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063693/ http://www.ncbi.nlm.nih.gov/pubmed/32158056 https://doi.org/10.1016/j.rse.2019.111584 |
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ENVELOPE(12.615,12.615,65.816,65.816) |
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Merra |
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Merra |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sens Environ |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063693/ http://www.ncbi.nlm.nih.gov/pubmed/32158056 http://dx.doi.org/10.1016/j.rse.2019.111584 |
op_doi |
https://doi.org/10.1016/j.rse.2019.111584 |
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Remote Sensing of Environment |
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111584 |
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