Grid-enabled high-performance quantitative aerosol retrieval from remotely sensed data

Authors:
Yong Xue;Jianwen Ai;Wei Wan;Huadong Guo;Yingjie Li;Ying Wang;Jie Guang;Linlu Mei;Hui Xu
Affiliations:
Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, No.9 Beiyitiao Road, Zhongguancun, Haidian District, Beijing 100080, China and Faculty of Computing, London Metropolita ...;State Key Laboratory of Remote-Sensing Science, jointly sponsored by the Institute of Remote-Sensing Applications (IRSA) of the Chinese Academy of Sciences (CAS) and Beijing Normal University, IRS ...;China Center for Resource Satellite Data and Application, Beijing 100830, China;Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, No.9 Beiyitiao Road, Zhongguancun, Haidian District, Beijing 100080, China;State Key Laboratory of Remote-Sensing Science, jointly sponsored by the Institute of Remote-Sensing Applications (IRSA) of the Chinese Academy of Sciences (CAS) and Beijing Normal University, IRS ...;State Key Laboratory of Remote-Sensing Science, jointly sponsored by the Institute of Remote-Sensing Applications (IRSA) of the Chinese Academy of Sciences (CAS) and Beijing Normal University, IRS ...;State Key Laboratory of Remote-Sensing Science, jointly sponsored by the Institute of Remote-Sensing Applications (IRSA) of the Chinese Academy of Sciences (CAS) and Beijing Normal University, IRS ...;State Key Laboratory of Remote-Sensing Science, jointly sponsored by the Institute of Remote-Sensing Applications (IRSA) of the Chinese Academy of Sciences (CAS) and Beijing Normal University, IRS ...;State Key Laboratory of Remote-Sensing Science, jointly sponsored by the Institute of Remote-Sensing Applications (IRSA) of the Chinese Academy of Sciences (CAS) and Beijing Normal University, IRS ...
Venue:
Computers & Geosciences
Year:
2011

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Abstract

As the quality and accuracy of remote-sensing instruments improve, the ability to quickly process remotely sensed data is in increasing demand. Quantitative remote-sensing retrieval is a complex computing process because of the terabytes or petabytes of data processed and the tight-coupling remote-sensing algorithms. In this paper, we intend to demonstrate the use of grid computing for quantitative remote-sensing retrieval applications with a workload estimation and task partition algorithm. Using a grid workflow for the quantitative remote-sensing retrieval service is an intuitive way to use the grid service for users without grid expertise. A case study showed that significant improvement in the system performance could be achieved with this implementation. The results of the case study also give a perspective on the potential of applying grid computing practices to remote-sensing problems.