Abstract
With the development of earth observation technologies and the construction of earth observation systems, an increasing amount of remote sensing data are being obtained. These provide the datasets required for research on remote sensing monitoring across large areas. To compensate for the shortcomings of global and large-area temporal monitoring data, synergized computing using multi-source remote sensing data can improve the accuracy and temporal resolution of remote sensing monitoring. However, remote sensing data are drawn from multiple sources and multiple scales, and have a complex structure and large volume; in addition, the nested system architecture of multi-source synergized remote sensing products makes the design of large-scale multi-source synergized remote sensing monitoring systems difficult. In this paper, we describe the design and implementation of a distributed parallel processing system for multi-source data synergized quantitative remote sensing based on a distributed cluster platform. The system integrates the algorithms normalizing more than 30 kinds of data sources and producing 40 quantitative remote sensing products. The system also connects a number of centers for satellite data, serves for several applications, and implements dynamic expansion integration for highly efficient quantitative remote sensing products. The system has produced approximately 50 TB of quantitative remote sensing products, and the application of these data to agriculture, forestry, the environment, and water conservation has resulted in very positive effects.
Highlights
With the development of earth observation technologies and the construction of earth observation systems, the total amount of remote sensing data has increased exponentially, and remote sensing has entered the era of big data
The earth observing systems operated by the US National Aeronautics and Space Administration (NASA) [1], National Oceanic and Atmospheric Administration (NOAA) [2], and the China High-resolution Earth Observation System (CHEOS) [3] receive and distribute a large number of remote sensing data, in the
Under the support of China’s National High Technology Research and Development (‘‘863’’) Program, which considers systems for VOLUME 8, 2020 satellite-aircraft-ground comprehensive quantitative remote sensing and its applications, we have developed a Distributed Parallel Processing System for Multi-source data with Synergized and Quantitative remote sensing (Dps-MuSyQ)
Summary
With the development of earth observation technologies and the construction of earth observation systems, the total amount of remote sensing data has increased exponentially, and remote sensing has entered the era of big data. Under the support of China’s National High Technology Research and Development (‘‘863’’) Program, which considers systems for VOLUME 8, 2020 satellite-aircraft-ground comprehensive quantitative remote sensing and its applications, we have developed a Distributed Parallel Processing System for Multi-source data with Synergized and Quantitative remote sensing (Dps-MuSyQ). The proposed system produces 40 kinds of multi-source synergized quantitative remote sensing products and more than 30 normalized products on the global and regional scale. It requires multi-source data to be used collaboratively across multiple data centers and can form a sustained, long-term production capacity. This paper describes the design and construction of Dps-MuSyQ based on the distributed cluster platform, and discusses the implementation of the highperformance production of multi-source quantitative remote sensing products
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