A Parallel and Optimization Approach for Land-Surface Temperature Retrieval on a Windows-Based PC Cluster

Bo Tie, Fang Huang, Jian Tao, Jun Lu and Dongwei Qiu
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Bo Tie: School of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., West Hi-Tech Zone, Chengdu 611731, China
Fang Huang: School of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., West Hi-Tech Zone, Chengdu 611731, China
Jian Tao: Texas A&M Engineering Experiment Station and High Performance Research Computing, Texas A&M University, College Station, TX 77843, USA
Jun Lu: School of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., West Hi-Tech Zone, Chengdu 611731, China
Dongwei Qiu: School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Xicheng District, Beijing100044, China

Sustainability, 2018, vol. 10, issue 3, 1-17

Abstract: Land-surface temperature (LST) is a very important parameter in the geosciences. Conventional LST retrieval is based on large-scale remote-sensing (RS) images where split-window algorithms are usually employed via a traditional stand-alone method. When using the environment to visualize images (ENVI) software to carry out LST retrieval of large time-series datasets of infrared RS images, the processing time taken for traditional stand-alone servers becomes untenable. To address this shortcoming, cluster-based parallel computing is an ideal solution. However, traditional parallel computing is mostly based on the Linux environment, while the LST algorithm developed within the ENVI interactive data language (IDL) can only be run in the Windows environment in our project. To address this problem, we combine the characteristics of LST algorithms with parallel computing, and propose the design and implementation of a parallel LST retrieval algorithm using the message-passing interface (MPI) parallel-programming model on a Windows-based PC cluster platform. Furthermore, we present our solutions to the problems associated with performance bottlenecks and fault tolerance during the deployment stage. Our results show that, by improving the parallel environment of the storage system and network, one can effectively solve the stability issues of the parallel environment for large-scale RS data processing.

Keywords: land-surface temperature retrieving; ENVI interactive data language (IDL); parallel computing; windows-based PC cluster; message passing interface (MPI); Samba (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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