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

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

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 comp...

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Bibliographic Details
Main Authors: Allan C. Just, Margherita M. De Carli, Alexandra Shtein, Michael Dorman, Alexei Lyapustin, Itai Kloog
Format: Article
Language:English
Published: MDPI AG 2018-05-01
Series:Remote Sensing
Subjects:
aerosol optical depth (AOD)
MAIAC
gradient boosting
AERONET
machine learning
PM2.5
MODIS
air pollution
measurement error
Online Access:http://www.mdpi.com/2072-4292/10/5/803

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