Name
Title | ||
|---|---|---|
Evaluation of a MISR-Based High-Resolution Aerosol Retrieval Method Using AERONET DRAGON Campaign Data |
Abstract | ||
|---|---|---|
Satellite-retrieved aerosol optical depth (AOD) can potentially provide an effective way to complement the spatial coverage limitation of a ground particulate air-pollution monitoring network such as the U.S. Environment Protection Agency's regulatory monitoring network. One of the current state-of-the-art AOD retrieval methods is the National Aeronautics and Space Administration's Multiangle Imaging SpectroRadiometer (MISR) operational algorithm, which has a spatial resolution of 17.6 km × 17.6 km. Although the MISR's aerosol products lead to exciting research opportunities to study particle composition at a regional scale, its spatial resolution is too coarse for analyzing urban areas, where the air pollution has stronger spatial variations and can severely impact public health and the environment. Accordingly, a novel AOD retrieval algorithm with a resolution of 4.4 km × 4.4 km has been recently developed, which is based on hierarchical Bayesian modeling and the Monte Carlo Markov chain (MCMC) inference method. In this paper, we carry out detailed quantitative and qualitative evaluations of the new algorithm, which is called the HB-MCMC algorithm, using recent AErosol RObotic NETwork (AERONET) Distributed Regional Aerosol Gridded Observation Networks (DRAGON) campaign data obtained in the summer of 2011. These data, which were not available in a previous study, contain spatially dense ground measurements of the AOD and other aerosol particle characteristics from the Baltimore-Washington, DC region. Our results show that the HB-MCMC algorithm has 16.2% more AOD retrieval coverage and improves the root-mean-square error by 38.3% compared with the MISR operational algorithm. Our detailed analyses with various metrics show that the improvement of our scheme is coming from the novel modeling and inference method. Furthermore, the map overlay of the retrieval results qualitatively confirms the findings of the quantitative analyses. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TGRS.2015.2395722 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
aod retrieval methods,atmospheric measuring apparatus,baltimore-washington,mcmc inference method,monte carlo markov chain,remote sensing,regulatory monitoring network,aod ground measurements,misr aerosol products,nasa misr operational algorithm,district of columbia region,root-mean-square error,hierarchical bayesian modeling,bayesian method,bayes methods,distributed regional aerosol gridded observation networks,air pollution,multiangle imaging spectroradiometer,monte carlo methods,aerosol robotic network,air pollution public health impact,hb-mcmc algorithm evaluation,high-resolution aerosol retrieval,aerosol optical depth,aerosols,aeronet dragon campaign data,high-resolution aerosol retrieval method,multiangle imaging spectroradiometer (misr),monte carlo markov chain (mcmc),national aeronautics and space administration misr,radiometers,air pollution environment impact,united states environment protection agency,ground particulate air-pollution monitoring network,satellite-retrieved aod,aerosol particle characteristics,air quality,particle composition,markov processes,vectors,spatial resolution | Meteorology,AERONET,Optical depth,Bayesian inference,Markov process,Markov chain Monte Carlo,Aerosol,Remote sensing,Spectroradiometer,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
53 | 8 | 0196-2892 |
Citations | PageRank | References |
1 | 0.38 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taesup Moon | 1 | 190 | 20.59 |
| Yueqing Wang | 2 | 40 | 7.86 |
| Yang Liu | 3 | 5 | 2.82 |
| Bin Yu | 4 | 1984 | 241.03 |