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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.

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