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Published： 07 January 2021 ，
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付东杰，肖寒，苏奋振，周成虎，董金玮，曾也鲁，闫凯，李世卫，吴进，吴文周，颜凤芹.2021.遥感云计算平台发展及地球科学应用.遥感学报，25（1）： 220-230
Fu D J，Xiao H，Su F Z，Zhou C H，Dong J W，Zeng Y L，Yan K，Li S W，Wu J，Wu W Z and Yan F Q. 2021. Remote sensing cloud computing platform development and Earth science application. National Remote Sensing Bulletin， 25（1）：220-230
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 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.
遥感大数据遥感云计算平台地球科学应用
remote sensingbig dataremote sensing cloud computing platformearth science application
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