期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3051799
关键词
Active-passive fusion; aerosol optical depth (AOD) recovery; Bayesian maximum entropy; large scale; Moderate Resolution Imaging Spectroradiometer (MODIS)
类别
资金
- National Key Research and Development Program of China [2017YFC0212601, 2016YFC0200900]
- China Postdoctoral Science Foundation [2020M682486]
- National Natural Science Foundation of China [41571344]
This study proposes a spatiotemporal AOD fusion framework based on Bayesian maximum entropy methodology, which combines active and passive remote sensing to provide satellite-derived AOD data sets with high spatial coverage and accuracy. Results demonstrate that the fusion algorithm significantly improves the spatial coverage of AOD and maintains the accuracy of the original satellite AOD products.
Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%-92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.
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