DOI: 10.1016/j.atmosenv.2014.06.019
Scopus记录号: 2-s2.0-84902441812
论文题名: China Collection 2.0: The aerosol optical depth dataset from the synergetic retrieval of aerosol properties algorithm
作者: Xue Y ; , He X ; , Xu H ; , Guang J ; , Guo J ; , Mei L
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 95 起始页码: 45
结束页码: 58
语种: 英语
英文关键词: Aerosol optical depth
; China Collection 2.0
; Cloud mask
; Gas absorption
; MODIS
; SRAP
Scopus关键词: Algorithms
; Atmospheric aerosols
; Gas absorption
; NASA
; Satellite imagery
; Surface measurement
; Aerosol optical depths
; China Collection 2.0
; Cloud masks
; MODIS
; SRAP
; Radiometers
; aerosol
; algorithm
; data set
; land surface
; MODIS
; optical depth
; remote sensing
; satellite imagery
; spatial resolution
; aerosol
; air pollution
; air quality
; algorithm
; article
; autumn
; China
; data processing
; desert
; dispersion
; dust
; economic development
; electric power plant
; environmental protection
; human activities
; humidity
; information retrieval
; optical depth
; photometer
; population density
; priority journal
; remote sensing
; rural population
; spring
; summer
; sun
; systematic error
; time series analysis
; winter
; China
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 5.0 and Collection 5.1 at 10km spatial resolution. The Collection 6.0 will be published soon with spatial resolution of 3km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved AOD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.0, dated from August 2002 to August 2012, and compared the AOD results with AErosol Robotic NETwork (AERONET) and Chinese Meteorological Administration Aerosol Remote Sensing Network (CARSNET) measurements. We find that 62% of China Collection 2.0 AOD values are within an expected error (EE) range of ±(0.05+20%) and that 56% are within an EE range of ±(0.05+15%) when compared with AERONET-observed values. For CARSNET validation, we find that 60% of China Collection 2.0 AOD values are within an expected error (EE) range of ±(0.05+20%) and that 53% are within an EE range of ±(0.05+15%). In addition, we also compare the AOD results with MODIS aerosol products, the cross validation shows that the two AOD have good consistency. Monthly averaged AOD results show that AOD is generally high in China's eastern coastal region from March to August, and AOD is not more than 0.5 in other months. Season averaged results show that the higher values of AOD are mostly distributed in eastern and southern China. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80862
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; Faculty of Life Sciences and Computing, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, United Kingdom; Centre for Atmosphere Watch and Services, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China; University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation:
Xue Y,, He X,, Xu H,et al. China Collection 2.0: The aerosol optical depth dataset from the synergetic retrieval of aerosol properties algorithm[J]. Atmospheric Environment,2014-01-01,95