遥感云计算平台发展及地球科学应用  

Remote sensing cloud computing platform development and Earth science application

作　　者：付东杰[1,3] 肖寒 苏奋振 周成虎[1,3] 董金玮 曾也鲁[4] 闫凯 李世卫[6] 吴进 吴文周 颜凤芹 FU Dongjie;XIAO Han;SU Fenzhen;ZHOU Chenghu;DONG Jinwei;ZENG Yelu;YAN Kai;LI Shiwei;WU Jin;WU Wenzhou;YAN Fengqin(State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy Sciences,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100101,China;Department of Global Ecology,Carnegie Institution for Science,Stanford CA 94305,USA;School of Land Science and Techniques,China University of Geosciences,Beijing 100083,China;Beijing Piesat Information Technology Co.,Ltd.,Beijing 100195,China)

机构地区：[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

出　　处：《遥感学报》2021年第1期220-230,共11页NATIONAL REMOTE SENSING BULLETIN

基　　金：中国科学院A类战略性先导科技专项(编号:XDA19060304)。

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。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 longterm 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 planetaryscale 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-CCP

关 键 词：遥感 大数据 遥感云计算平台 地球科学应用 

分 类 号：P237[天文地球—摄影测量与遥感] P3[天文地球—测绘科学与技术] TP79[自动化与计算机技术—检测技术与自动化装置]