Ensemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertainties

Journal article


Xie, Yanqing, Xue, Yong, Che, Yahui, Guang, Jie, Mei, Linlu, Voorhis, Dave, Fan, Cheng, She, Lu and Xu, Hui 2017. Ensemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertainties. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2017.2757910
AuthorsXie, Yanqing, Xue, Yong, Che, Yahui, Guang, Jie, Mei, Linlu, Voorhis, Dave, Fan, Cheng, She, Lu and Xu, Hui
Abstract

Within 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.

KeywordsRemote sensing; Satellites; Aerosol optical depth; Optical surface waves
Year2017
JournalIEEE Transactions on Geoscience and Remote Sensing
PublisherIEEE
ISSN01962892
15580644
Digital Object Identifier (DOI)https://doi.org/10.1109/TGRS.2017.2757910
Web address (URL)http://hdl.handle.net/10545/621924
http://creativecommons.org/licenses/by/4.0/
hdl:10545/621924
Publication dates20 Oct 2017
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Deposited27 Oct 2017, 15:27
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Archived with thanks to IEEE Transactions on Geoscience and Remote Sensing

ContributorsUniversity of Chinese Academy of Sciences, University of Derby, University of Bremen and Chinese Academy of Meteorological Sciences
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