ArticleRemote sensing of environment2020
Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling.
Article in Remote sensing of environment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 121 citations in OpenAlex.
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- Quality Assessment and Application Scenario Analysis of AGRI Land Aerosol Product from the Geostationary Satellite Fengyun-4B in China.Sensors (Basel, Switzerland) · 2024Article
- Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing.Communications earth & environment · 2023Article
- Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder.Scientific data · 2023Article
- Spatial Differentiation of PMInternational journal of environmental research and public health · 2023Article
- Sustainable urban systems: from landscape to ecological processes.Ecological processes · 2022Article
- A machine learning model to estimate ambient PMRemote sensing of environment · 2021Article
- Estimating PMRemote sensing of environment · 2021Article
- Ensemble-based deep learning for estimating PMEnvironment international · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.