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Remote sensing cloud computing platform development and Earth science application

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

  • Email：fudj@lreis.ac.cn
  • Introduction：付东杰，1985年生, 男, 助理研究员,研究方向为定量遥感。E-mail: fudj@lreis.ac.cn
FU Dongjie13,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

XIAO Han13,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

  • Email：sufz@lreis.ac.cn
  • Introduction：苏奋振，1972年生，男，研究员，研究方向为地理信息与遥感评估、海岸带与海洋利用、空间博弈与海洋战略。E-mail：sufz@lreis.ac.cn
SU Fenzhen13*,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

ZHOU Chenghu13,  
  • Affiliation：

    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

DONG Jinwei23,  
  • Affiliation：

    Department of Global Ecology, Carnegie Institution for Science, Stanford CA 94305, USA

ZENG Yelu4,  
  • Affiliation：

    School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China

YAN Kai5,  
  • Affiliation：

    Beijing Piesat Information Technology Co.,Ltd. , Beijing 100195, China

LI Shiwei6,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

WU Jin13,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

WU Wenzhou13,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China

YAN Fengqin13

resumen

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.

palabra clave

remote sensing;big data;remote sensing cloud computing platform;earth science application

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