Fast Aerosol Optical Depth Retrieval from MODIS

• Published in: IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium Vol. 3; pp. III - 927 - III - 930
• Main Authors: Jianping Guo, Huadong Xiao, Chunxiang Cao, Yong Xue, Jie Guang, Jianwen Ai, Xiaozhou Xin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2008
• Subjects: Aerosols; Algorithm design and analysis; AOD; Domain decomposition; Geophysics computing; Image retrieval; Information retrieval; Meteorology; MODIS; Optical computing; Parallel algorithms; Parallel Computation; Satellites; SPMD
• ISBN: 1424428076; 9781424428076
• ISSN: 2153-6996; 2153-7003
• DOI: 10.1109/IGARSS.2008.4779502

Aerosol optical depth (hearafter called AOD) is an important geophysical parameter, and its retrieval from satellite data are both data and computationally intensive. This paper presents the implementation of parallel AOD retrieval from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, based on IBM System Cluster 1600 deployed in Chinese Meteorological Administration (CMA), with focuses on the design of parallel algorithm through Single Program Multiple Data (SPMD) model. In order to demonstrate the parallel performance of the proposed parallel implementation, experiment of parallel AOD retrieval is given, and results show that the implementation of AOD parallel computing, as a viable cost-effective method, has great scalability on clusters machines, which can obtain optimum performance at 128 processors. Meanwhile, the parallel algorithm can be operationally used in AOD retrieval in the near future, which is an important input parameter for the air quality model.