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Grid-enabled high-performance quantitative aerosol retrieval from remotely sensed data |
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| Authors: | Yong Xue Jianwen Ai Wei WanHuadong Guo Yingjie Li Ying Wang Jie Guang Linlu Mei Hui Xu |
| Affiliation: | a Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, No.9 Beiyitiao Road, Zhongguancun, Haidian District, Beijing 100080, China b Faculty of Computing, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, UK c 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, IRSA, CAS, P.O. Box 9718, Beijing 100101, China d China Center for Resource Satellite Data and Application, Beijing 100830, China e Graduate University of the CAS, Beijing, China |
| 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. |
| Keywords: | Aerosol optical thickness Grid computing High-throughput computing Remote-sensing. |
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