DOI: 10.1002/jgrd.50712
论文题名: Enhanced Deep Blue aerosol retrieval algorithm: The second generation
作者: Hsu N.C. ; Jeong M.-J. ; Bettenhausen C. ; Sayer A.M. ; Hansell R. ; Seftor C.S. ; Huang J. ; Tsay S.-C.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 16 起始页码: 9296
结束页码: 9315
语种: 英语
Scopus关键词: Algorithms
; Arid regions
; Imaging techniques
; Radiometers
; Reflection
; Satellite imagery
; Supercomputers
; Aerosol optical thickness
; Aerosol retrieval algorithms
; Aerosol robotic networks
; Arid and semi-arid regions
; Moderate resolution imaging spectroradiometer
; Normalized difference vegetation index
; Retrieval algorithms
; Sea-viewing wide field-of-view sensors
; Aerosols
; AERONET
; aerosol
; algorithm
; brightness temperature
; cloud microphysics
; desert
; land cover
; land surface
; MODIS
; NDVI
; optical depth
; seasonal variation
; SeaWiFS
; semiarid region
; surface reflectance
; time series
; urban region
英文摘要: The aerosol products retrieved using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semiarid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and nonvegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of precalculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semiarid regions to the entire land areas. In this paper, the changes made in the enhanced Deep Blue algorithm regarding the surface reflectance estimation, aerosol model selection, and cloud screening schemes for producing the MODIS collection 6 aerosol products are discussed. A similar approach has also been applied to the algorithm that generates the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Deep Blue products. Based upon our preliminary results of comparing the enhanced Deep Blue aerosol products with the Aerosol Robotic Network (AERONET) measurements, the expected error of the Deep Blue aerosol optical thickness (AOT) is estimated to be better than 0.05 + 20%. Using 10 AERONET sites with long-term time series, 79% of the best quality Deep Blue AOT values are found to fall within this expected error. © 2013. Her Majesty the Queen in Right of Canada. American Geophysical Union. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63413
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States; Gangneung-Wonju National University, Gangneung City, South Korea; Science Systems and Applications, Inc, Lanham, MD, United States; Goddard Earth Sciences Technology and Research, Universities Space Research Association, Greenbelt, MD, United States; Earth System Science Interdisciplinary Center, UMD, College Park, MD, United States; NOAA NESDIS Center for Satellite Applications and Research, College Park, MD, United States
Recommended Citation:
Hsu N.C.,Jeong M.-J.,Bettenhausen C.,et al. Enhanced Deep Blue aerosol retrieval algorithm: The second generation[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(16)