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Open AccessArticle

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

by Lianfa Li 1,2,3
1
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Spatial Data Intelligence Lab Ltd. Liability Co., Casper, WY 82609, USA
Remote Sens. 2020, 12(2), 264; https://doi.org/10.3390/rs12020264
Received: 22 November 2019 / Revised: 1 January 2020 / Accepted: 7 January 2020 / Published: 13 January 2020
(This article belongs to the Section Atmosphere Remote Sensing)
Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values. View Full-Text
Keywords: PM2.5; satellite AOD; deep learning; autoencoder; residual network; exposure estimation; high spatiotemporal resolution PM2.5; satellite AOD; deep learning; autoencoder; residual network; exposure estimation; high spatiotemporal resolution
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MDPI and ACS Style

Li, L. A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5. Remote Sens. 2020, 12, 264.

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