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dc.contributor.authorXie, Yanqing
dc.contributor.authorXue, Yong
dc.contributor.authorChe, Yahui
dc.contributor.authorGuang, Jie
dc.contributor.authorMei, Linlu
dc.contributor.authorVoorhis, Dave
dc.contributor.authorFan, Cheng
dc.contributor.authorShe, Lu
dc.contributor.authorXu, Hui
dc.date.accessioned2017-10-27T15:27:16Z
dc.date.available2017-10-27T15:27:16Z
dc.date.issued2017-10-20
dc.identifier.citationXie, Y. et al (2017) 'Ensemble of ESA/AATSR Aerosol Optical Depth Products Based on the Likelihood Estimate Method With Uncertainties' IEEE Transactions on Geoscience and Remote Sensing, 56(2) pp. 997-1007.en
dc.identifier.issn01962892
dc.identifier.doi10.1109/TGRS.2017.2757910
dc.identifier.urihttp://hdl.handle.net/10545/621924
dc.description.abstractWithin the European Space Agency Climate Change Initiative (CCI) project Aerosol_cci, there are three aerosol optical depth (AOD) data sets of Advanced Along-Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute, the Oxford-Rutherford Appleton Laboratory (RAL) Retrieval of Aerosol and Cloud (ORAC) algorithm by the University of Oxford/RAL, and the Swansea algorithm (SU) by the University of Swansea. The three AOD data sets vary widely. Each has unique characteristics: the spatial coverage of ORAC is greater, but the accuracy of ADV and SU is higher, so none is significantly better than the others, and each has shortcomings that limit the scope of its application. To address this, we propose a method for converging these three products to create a single data set with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the systematic errors; the second part is to calculate the uncertainty and fusion of data sets using the maximum likelihood estimate method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused data set after mask is 148%, 13%, and 181% higher than those of ADV, ORAC, and SU, respectively, and the root-mean-square error, mean absolute error, mean bias error, and relative mean bias are superior to those of the three original data sets. Thus, the accuracy and spatial coverage of the fused AOD data set masked with a threshold of 0.12 are improved compared to the original data set. Finally, we discuss the selection of mask thresholds.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8077768/en
dc.rightsArchived with thanks to IEEE Transactions on Geoscience and Remote Sensingen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRemote sensingen
dc.subjectSatellitesen
dc.subjectAerosol optical depthen
dc.subjectOptical surface wavesen
dc.titleEnsemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertaintiesen
dc.typeArticleen
dc.identifier.eissn15580644
dc.contributor.departmentUniversity of Chinese Academy of Sciencesen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentUniversity of Bremenen
dc.contributor.departmentChinese Academy of Meteorological Sciencesen
dc.identifier.journalIEEE Transactions on Geoscience and Remote Sensingen
html.description.abstractWithin the European Space Agency Climate Change Initiative (CCI) project Aerosol_cci, there are three aerosol optical depth (AOD) data sets of Advanced Along-Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute, the Oxford-Rutherford Appleton Laboratory (RAL) Retrieval of Aerosol and Cloud (ORAC) algorithm by the University of Oxford/RAL, and the Swansea algorithm (SU) by the University of Swansea. The three AOD data sets vary widely. Each has unique characteristics: the spatial coverage of ORAC is greater, but the accuracy of ADV and SU is higher, so none is significantly better than the others, and each has shortcomings that limit the scope of its application. To address this, we propose a method for converging these three products to create a single data set with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the systematic errors; the second part is to calculate the uncertainty and fusion of data sets using the maximum likelihood estimate method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused data set after mask is 148%, 13%, and 181% higher than those of ADV, ORAC, and SU, respectively, and the root-mean-square error, mean absolute error, mean bias error, and relative mean bias are superior to those of the three original data sets. Thus, the accuracy and spatial coverage of the fused AOD data set masked with a threshold of 0.12 are improved compared to the original data set. Finally, we discuss the selection of mask thresholds.


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