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.
Highlights
Land-surface temperature (LST) is a very important parameter in the geosciences, which plays a fundamental role in land–atmosphere interaction and is a key parameter in global environmental change in terms of its effect on global hydrology, ecology and biogeochemical processes [1,2,3]
The RS data that are applied to LST retrieval are provided by several instruments including the Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+), the Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER), the Moderate-ResOlution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR)
We propose a parallel algorithm for LST retrieval at the process level using an message-passing interface (MPI) parallel-programming model, which enables us to perform high-performance LST retrieval in a distributed memory environment with the Windows operating system
Summary
Land-surface temperature (LST) is a very important parameter in the geosciences, which plays a fundamental role in land–atmosphere interaction and is a key parameter in global environmental change in terms of its effect on global hydrology, ecology and biogeochemical processes [1,2,3]. Our results include: (1) the global products of essential land variables for 33 years (from 1982 to 2014), including the leaf-area index, emissivity, surface albedo, and photosynthetically active radiation; and (2) another eight products for four representative years (1983, 1993, 2003 and 2013), which involve shortwave radiation downstream of photosynthetically active radiation, LSTs, net long-wave radiation, net radiation (daytime), vegetation coverage, gross primary productivity, and latent heat [24] Within this big project, our work is mainly focused on creating a new and integrated LST-retrieval algorithm that is suitable for generating long time series of LST products based on RS data, because these products have extremely important practical value for climate-change modeling, surface radiation and regional/global energy balance. These selected algorithms have advantages such as high precision, low sensitivity on the initial values of the inputted parameters, and highly practicability; (2) we built an integrated algorithm with a BMA model, as the BMA method has several advantages related to the integration of surface long-wave radiation models [50] and evapotranspiration model integration [51]
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