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Grid Service Implementation of Aerosol Optical Thickness Retrieval over Land from MODIS

  • Yincui Hu
  • Yong Xue
  • Guoyin Cai
  • Chaolin Wu
  • Jianping Guo
  • Ying Luo
  • Wei Wan
  • Lei Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3983)

Abstract

To derive the actual land surface information quantitatively, the atmospheric effects should be correctly removed. Atmospheric effects dependent on aerosol particles, clouds and other atmosphere conditions. Aerosol parameters can be retrieved from the remotely sensed data. The retrieved aerosol characters can also be applied to environmental monitoring. To retrieval the aerosol optical thickness over land, many methods have been developed. The most popular one is the dark dense vegetation method. But it is confined to vegetation fields. The SYNTAM method can be used to retrieval aerosol optical thickness over land from MODIS data, no matter whether the land is dark or bright. In this paper, the SYNTAM method is applied to MODIS data for the retrieval of aerosol optical thickness over China. The retrieval process is complicated. And the EMS memory required is too large for a personal computing to run successfully. To solve this problem, the Grid environment is used. Our experiments were performed on the High-Throughput Spatial Information Processing Prototype System based on Grid platform in Institute of Remote Sensing Applications, Chinese Academy of Sciences. The aerosol optical thickness retrieval process is described in this paper. And the detail data query, data pre-processing, job monitoring and post-processing is discussed. Moreover, test results are also reported in this paper.

Keywords

Remote Sensing MODIS Data Grid Resource Grid Environment Aerosol Optical Thickness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yincui Hu
    • 1
  • Yong Xue
    • 1
    • 2
  • Guoyin Cai
    • 1
  • Chaolin Wu
    • 1
  • Jianping Guo
    • 1
    • 3
  • Ying Luo
    • 1
    • 3
  • Wei Wan
    • 1
    • 3
  • Lei Zheng
    • 1
    • 3
  1. 1.State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of, Chinese Academy of Sciences and Beijing Normal University, Institute of Remote Sensing ApplicationsChinese Academy of SciencesBeijingChina
  2. 2.Department of ComputingLondon Metropolitan UniversityLondonUK
  3. 3.Graduate School of the Chinese Academy of SciencesBeijingChina

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