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Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA

1
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
2
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
3
National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 803; https://doi.org/10.3390/rs10050803
Received: 3 April 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 22 May 2018
(This article belongs to the Special Issue Aerosol Remote Sensing)
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Abstract

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30–210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling. View Full-Text
Keywords: aerosol optical depth (AOD); MAIAC; gradient boosting; AERONET; machine learning; PM2.5; MODIS; air pollution; measurement error aerosol optical depth (AOD); MAIAC; gradient boosting; AERONET; machine learning; PM2.5; MODIS; air pollution; measurement error
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Just, A.C.; De Carli, M.M.; Shtein, A.; Dorman, M.; Lyapustin, A.; Kloog, I. Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA. Remote Sens. 2018, 10, 803.

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