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Yao, Fei, Si, Menglin, Li, Weifeng, Wu, Jiansheng (2018) A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. Science of The Total Environment, 618. 819-828 doi:10.1016/j.scitotenv.2017.08.209

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Reference TypeJournal (article/letter/editorial)
TitleA multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China
JournalScience of The Total Environment
AuthorsYao, FeiAuthor
Si, MenglinAuthor
Li, WeifengAuthor
Wu, JianshengAuthor
Year2018 (March)Volume618
PublisherElsevier BV
DOIdoi:10.1016/j.scitotenv.2017.08.209Search in ResearchGate
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Mindat Ref. ID3833332Long-form Identifiermindat:1:5:3833332:1
GUID0
Full ReferenceYao, Fei, Si, Menglin, Li, Weifeng, Wu, Jiansheng (2018) A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. Science of The Total Environment, 618. 819-828 doi:10.1016/j.scitotenv.2017.08.209
Plain TextYao, Fei, Si, Menglin, Li, Weifeng, Wu, Jiansheng (2018) A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. Science of The Total Environment, 618. 819-828 doi:10.1016/j.scitotenv.2017.08.209
In(2018) Science of The Total Environment Vol. 618. Elsevier BV

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Wu, Jiansheng, Yao, Fei, Li, Weifeng, Si, Menglin (2016) VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model. Remote Sensing of Environment, 184. 316-328 doi:10.1016/j.rse.2016.07.015
Yao, Fei, Wu, Jiansheng, Li, Weifeng, Peng, Jian (2019) A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China. ISPRS Journal of Photogrammetry and Remote Sensing, 151. 263-276 doi:10.1016/j.isprsjprs.2019.03.011
Yao, Fei, Wu, Jiansheng, Li, Weifeng, Peng, Jian (2019) Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model. Remote Sensing, 11 (7). doi:10.3390/rs11070841
Lv, Baolei, Hu, Yongtao, Chang, Howard H., Russell, Armistead G., Cai, Jun, Xu, Bing, Bai, Yuqi (2017) Daily estimation of ground-level PM2.5 concentrations at 4 km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations. Science of The Total Environment, 580. 235-244 doi:10.1016/j.scitotenv.2016.12.049
Yao, Fei, Palmer, Paul I. (2021) A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD. Atmospheric Environment, 248. 118217pp. doi:10.1016/j.atmosenv.2021.118217
You, Wei, Zang, Zengliang, Pan, Xiaobin, Zhang, Lifeng, Chen, Dan (2015) Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models. Science of The Total Environment, 505. 1156-1165 doi:10.1016/j.scitotenv.2014.11.024
Zang, Zengliang, Wang, Weiqi, You, Wei, Li, Yi, Ye, Fang, Wang, Chunming (2017) Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. Science of The Total Environment, 575. 1219-1227 doi:10.1016/j.scitotenv.2016.09.186
Wu, Jiansheng, Liang, Jingtian, Zhou, Liguo, Yao, Fei, Peng, Jian (2019) Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China. Remote Sensing, 11 (19). doi:10.3390/rs11192223
Wang, Xinpeng, Sun, Wenbin, Zheng, Kangning, Ren, Xiangyu, Han, Peiwen (2020) Estimating hourly PM2.5 concentrations using MODIS 3 km AOD and an improved spatiotemporal model over Beijing-Tianjin-Hebei, China. Atmospheric Environment, 222. 117089pp. doi:10.1016/j.atmosenv.2019.117089
Liu, Jun, Han, Yiqun, Tang, Xiao, Zhu, Jiang, Zhu, Tong (2016) Estimating adult mortality attributable to PM2.5 exposure in China with assimilated PM2.5 concentrations based on a ground monitoring network. Science of The Total Environment, 568. 1253-1262 doi:10.1016/j.scitotenv.2016.05.165
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