地球科学进展 ›› 2015, Vol. 30 ›› Issue (7): 751 -762. doi: 10.11867/j.issn.1001-8166.2015.07.0751

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遥感的科学推动作用与重点应用领域
吴炳方( ), 邢强   
  1. 中国科学院遥感与数字地球研究所,中国科学院数字地球重点实验室,北京100101
  • 出版日期:2015-07-20
  • 基金资助:
    国家自然科学基金面上项目“空气动力学粗糙度多源数据协同反演模型研究”(编号: 41271424);国家自然科学基金黑河流域生态—水文过程集成研究重大研究计划“干旱区陆表蒸散遥感估算的参数化方法研究”(编号:91025007)资助

Remote Sensing Roles on Driving Science and Major Applications

Bingfang Wu( ), Qiang Xing   

  1. Key laboratory of Digital Earth Sciences, Chinese Academy of Sciences Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing100101, China
  • Online:2015-07-20 Published:2015-07-20

在调研国内外遥感案例的基础上,论述了遥感在推动地球系统科学发展方面的作用,及在我国的重点应用领域。遥感催生了全球变化研究,使得人类得以从新的视角来探索地球上的生命未来;遥感推动了地球科学从定性到定量、从描述到分析、从单站点到多时空尺度的变革,诸多新兴交叉学科应运而生。遥感是应用驱动的, 一致性及可对比性是定量遥感的核心,也是遥感深化应用的基础。遥感应用于众多领域,但不同的国家基于各自的国情有不同的侧重点,其中,维护国家全球利益、灾害快速响应与灾后评估、第三方独立监督、保障国防安全是我国的应用重点。

This paper aims at discussing the roles of remote sensing on driving the development of Earth System Science and the major application domains based on domestic and foreign remote sensing cases. Remote sensing has led to the development of the research on global change, making human beings being capable of exploring the future of the life on earth with a new perspective. Remote sensing has also driven the transformation of Earth Science from qualitative analysis to quantitative development, from description to further analysis, from single site to regional application with multiple temporal and spatial scales. As a result, it leads to the appearance of many emerging cross-disciplines. Remote sensing is an application-driven subject, with numerous application fields after many years of development. However, different countries in the world have different priorities, which are dependent on their own national conditions. For China, safeguarding national global interests, rapid response to disasters and assessment after disasters, independent supervision from the third party and safeguarding national defense security are the major applications. Remote Sensing data consistency is of crucial importance in order to allow for time series comparison analysis and anomaly detection, which is recognized as the core of quantitative remote sensing and also the basis for driving remote sensing applications to a deeper level continually.

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