The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products

被引:70
|
作者
Zhao, Xiang [1 ,2 ]
Liang, Shunlin [2 ,3 ]
Liu, Suhong [2 ,4 ]
Yuan, Wenping [1 ]
Xiao, Zhiqiang [2 ,4 ]
Liu, Qiang [1 ,2 ]
Cheng, Jie [1 ,2 ]
Zhang, Xiaotong [1 ,2 ]
Tang, Hairong [5 ]
Zhang, Xin [2 ]
Liu, Qiang [1 ,2 ]
Zhou, Gongqi [2 ]
Xu, Shuai [1 ]
Yu, Kai [5 ]
机构
[1] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[2] Chinese Acad Sci, Beijing Normal Univ & Inst Remote Sensing Applica, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[3] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[4] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
[5] Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China
关键词
remote sensing; satellite data; product generation system; GLASS products; high performance computing; PHOTOSYNTHETICALLY ACTIVE RADIATION; TIME-SERIES DATA; MODIS; COVER; AVHRR; PHENOLOGY; ALBEDO; BAND;
D O I
10.3390/rs5052436
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved quantitatively through models or statistical methods. However, producing global products requires a complex computing system and massive volumes of multi-sensor and multi-temporal remotely sensed data. This manuscript describes the ground Global LAnd Surface Satellite (GLASS) product generation system that can be used to generate long-sequence time series of global land surface data products based on various remotely sensed data. To ensure stabilization and efficiency in running the system, we used the methods of task management, parallelization, and multi I/O channels. An array of GLASS remote sensing products related to global land surface parameters are currently being produced and distributed by the Center for Global Change Data Processing and Analysis at Beijing Normal University in Beijing, China. These products include Leaf Area Index (LAI), land surface albedo, and broadband emissivity (BBE) from the years 1981 to 2010, downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) from the years 2008 to 2010.
引用
收藏
页码:2436 / 2450
页数:15
相关论文
共 50 条
  • [41] Land surface temperature variability across India: a remote sensing satellite perspective
    Satya Prakash
    Hamid Norouzi
    [J]. Theoretical and Applied Climatology, 2020, 139 : 773 - 784
  • [42] Land surface temperature variability across India: a remote sensing satellite perspective
    Prakash, Satya
    Norouzi, Hamid
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2020, 139 (1-2) : 773 - 784
  • [43] An improved land surface emissivity parameter for land surface models using global remote sensing observations
    Jin, Menglin
    Liang, Shunlin
    [J]. JOURNAL OF CLIMATE, 2006, 19 (12) : 2867 - 2881
  • [44] Urban land cover changes assessment from satellite remote sensing data
    Zoran, M.
    [J]. REMOTE SENSING FOR A CHANGING EUROPE, 2009, : 467 - 474
  • [45] The Global Land Surface Temperature Change in the 21st Century-A Satellite Remote Sensing Based Assessment
    Lin, Li
    Di, Liping
    Zhang, Chen
    Guo, Liying
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 1756 - 1764
  • [46] Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
    Yu, Tao
    Sun, Rui
    Xiao, Zhiqiang
    Zhang, Qiang
    Liu, Gang
    Cui, Tianxiang
    Wang, Juanmin
    [J]. REMOTE SENSING, 2018, 10 (02)
  • [47] Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data
    Zhang, Xiaotong
    Liang, Shunlin
    Zhou, Gongqi
    Wu, Haoran
    Zhao, Xiang
    [J]. REMOTE SENSING OF ENVIRONMENT, 2014, 152 : 318 - 332
  • [48] Forest definitions collaboration based on global remote sensing data products
    Hu, Tao
    Peng, Jian
    Dong, Jianquan
    Xiao, Shancai
    Xia, Pei
    [J]. Dili Xuebao/Acta Geographica Sinica, 2024, 79 (05): : 1115 - 1128
  • [49] A new algorithm of retrieving the surface albedo by satellite remote sensing data
    Nikolaeva O.V.
    [J]. Atmospheric and Oceanic Optics, 2016, 29 (4) : 342 - 347
  • [50] China land soil moisture EnKF data assimilation based on satellite remote sensing data
    SHI ChunXiang1
    2 Institute of Atmospheric Physics
    3 Institute of Geology
    Key Geodynamics Laboratory
    4 National Meteorological Center
    [J]. Science China Earth Sciences, 2011, 54 (09) : 1430 - 1440