首页 >  2021, Vol. 25, Issue (1) : 220-230

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DOI:

10.11834/jrs.20210447

收稿日期:

2020-10-09

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遥感云计算平台发展及地球科学应用
付东杰1,3,肖寒1,3,苏奋振1,3,周成虎1,3,董金玮2,3,曾也鲁4,闫凯5,李世卫6,吴进1,3,吴文周1,3,颜凤芹1,3
1.中国科学院 地理科学与资源研究所 资源与环境信息系统国家重点实验室, 北京 100101;2.中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室, 北京 100101;3.中国科学院大学 资源与环境学院, 北京 100101;4.斯坦福卡内基研究所 全球生态学系, 加州 94305;5.中国地质大学(北京) 土地科学技术学院, 北京 100083;6.北京航天宏图信息技术股份有限公司, 北京 100195
摘要:

人类已有半个多世纪的全球历史遥感数据积累,这些不断涌现的海量遥感数据形成的遥感大数据为地球科学研究提供了丰富的数据支持;对遥感大数据快速处理、分析和挖掘是一个新的挑战。遥感云计算平台的出现为遥感大数据挖掘提供了前所未有的机遇,并彻底改变了传统遥感数据处理和分析的模式,使得全球尺度的长时间序列快速分析和应用成为可能。本文系统梳理了国内外遥感云计算平台发展现状,归纳了截止目前遥感云计算平台在地球科学领域应用的主要方向。在此基础上讨论了目前遥感云计算平台的局限性,并展望了未来需要解决的关键技术和核心问题,提出了中国遥感云计算平台发展的建议。随着人类对地球的认识需求提升,遥感云计算平台将会在地学研究中发挥更大的作用,服务于地学知识的深入及人类社会可持续发展。

Remote sensing cloud computing platform development and Earth science application
Abstract:

Global scale historical remote sensing data has been accumulated for more than half a century. The remote sensing big data formed by these continuously emerging massive remote sensing data provides abundant data support for Earth science research. Furthermore, it is a new challenge for the rapid processing, analysis and mining of remote sensing big data. The emergence of Remote Sensing Cloud Computing Platform (RS-CCP) provides unprecedented opportunities for remote sensing big data mining. Meanwhile, it completely changes the traditional remote sensing data processing and analysis mode, making it possible to quickly analyze and apply long-term sequences on a global scale.This study systematically combed the state-of-the-art development of Google Earth Engine (GEE), including the origin, current progress, petabyte scale catalog of public and free-to-use geospatial datasets, computing capability for planetary-scale analysis of Earth science data, Application Programming Interface (API), and GEE Apps. Combined with GEE, the RS-CCPs at home and abroad, including NASA Earth Exchange, Descartes Labs, Amazon Web Services (AWS), Data Cube, Copernicus Data and Exploitation Platform-DE (CODE-DE), CASEarth EarthDataMiner, Pixel Information Expert (PIE)-Engine, were analyzed from the aspects of public data achieve, platform type, and APIs. Meanwhile, the RS-CCP developed by Chinese Business Company were also taken into account, such as SenseEarth, Analytical Insight of Earth (AI EARTH), WeEath. Furthermore, this study summarized the main applications of RS-CCPs in the field of Earth sciences according to Amani et al. (2020) and Tamiminia et al. (2020). Specifically, the RS-CCPs based applications published on Nature (and its series), Science (and its series) and Proceedings of the National Academy of Sciences of the United States of America (PNAS) were summarized as applications related to land cover/land use, vegetation changes, animal, climate change, Human social and economic activities.On this basis, the limitations of current RS-CCPs were discussed, such as (1) Limited storage and computing resources, (2) Some geospatial data types are not compatible, (3) Insufficient support for different projection formats, (4) Difficult to achieve calculation between pixels, (5) Not support mobile applications, (6) The typesetting and drawing module is not perfect. The key technologies and core issues that need to be resolved in the future were prospected. Subsequently, some recommendations were provide for the development of China’s RS-CCP: (1) Integration of multi-source data resources, especially domestic remote sensing data, (2) Guarantee the quality and reliability of domestic remote sensing data, (3) Promote a new data-driven geoscience research paradigm. With the increasing demand of human understanding of the Earth, RS-CCPs will play a greater role in Earth science, serving the deepening of Earth science knowledge and the sustainable development of human society.

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