A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data

Atmospheric aerosols can cause serious damage to human health and reduce life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves aerosol optical depth (AOD) at 17.6 km resolution. A systematic study o...

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Bibliographic Details
Published in:Journal of the American Statistical Association
Main Authors: Wang, Yueqing, Jiang, Xin, Yu, Bin, Jiang, Ming
Other Authors: Wang, YQ (reprint author), Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA., Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA., Peking Univ, Sch Math Sci, LMAM, Beijing 100871, Peoples R China., Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA., Beijing Int Ctr Math Res, Beijing 100871, Peoples R China.
Format: Journal/Newspaper
Language:English
Published: journal of the american statistical association 2013
Subjects:
Fine retrieval resolution
Hierarchical Bayesian model
MCMC
Remote sensing
Spatial dependence
ATMOSPHERIC TURBIDITY
DISTRIBUTIONS
METROPOLITAN
AERONET
CLIMATE
Aerosol Robotic Network
Online Access:https://hdl.handle.net/20.500.11897/222770
https://doi.org/10.1080/01621459.2013.796834
Description
Summary:Atmospheric aerosols can cause serious damage to human health and reduce life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves aerosol optical depth (AOD) at 17.6 km resolution. A systematic study of aerosols and their impact on public health, especially in highly populated urban areas, requires finer-resolution estimates of AOD's spatial distribution. We embed MISR's operational weighted least squares criterion and its forward calculations for AOD retrievals in a likelihood framework and further expand into a hierarchical Bayesian model to adapt to finer spatial resolution of 4.4 km. To take advantage of AOD's spatial smoothness, our method borrows strength from data at neighboring areas by postulating a Gaussian Markov random field prior for AOD. Our model considers AOD and aerosol mixing vectors as continuous variables, whose inference is carried out using Metropolis-within-Gibbs sampling methods. Retrieval uncertainties are quantified by posterior variabilities. We also develop a parallel Markov chain Monte Carlo (MCMC) algorithm to improve computational efficiency. We assess our retrieval performance using ground-based measurements from the AErosol RObotic NETwork (AERONET) and satellite images from Google Earth. Based on case studies in the greater Beijing area, China, we show that 4.4 km resolution can improve both the accuracy and coverage of remotely sensed aerosol retrievals, as well as our understanding of the spatial and seasonal behaviors of aerosols. This is particularly important during high-AOD events, which often indicate severe air pollution. http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000321727700010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 Statistics & Probability SCI(E) 2 ARTICLE 502 483-493 108

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