地理学报 ›› 2017, Vol. 72 ›› Issue (11): 2093-2111.doi: 10.11821/dlxb201711013

• 遥感与GIS • 上一篇    

从遥感观测数据到数据产品

吴炳方(), 张淼   

  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 遥感科学国家重点实验室,北京 100101
  • 收稿日期:2017-01-10 修回日期:2017-08-23 出版日期:2017-11-20 发布日期:2017-11-16
  • 作者简介:

    作者简介:吴炳方(1962-), 男, 江西玉山人, 博士, 研究员, 主要研究领域包括农业遥感与粮食安全、水资源遥感与耗水管理、生态遥感等。E-mail: wubf@radi.ac.cn

  • 基金资助:
    国家重点研发计划(2016YFA0600304)

Remote sensing: Observations to data products

Bingfang WU(), Miao ZHANG   

  1. Key Laboratory of Digital Earth Science and State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2017-01-10 Revised:2017-08-23 Online:2017-11-20 Published:2017-11-16
  • Supported by:
    National Key R&D Program of China, No.2016YFA 0600304

摘要:

本文将遥感作为一种观测手段,通过梳理遥感从观测数据到数据产品的处理方法,分析了目标识别和参数提取所采用的方法、特点与存在的问题,发现遥感从观测数据到数据产品的过程至今仍未形成系统、科学的方法论,指出遥感方法论的建立需通过挖掘多源、多角度、多时相、多光谱、主被动协同的遥感观测数据隐含的深层指示性特征,加强结构化方法研究,构建新型的、可重复、易于处理且能够反映物理、化学、地学、生态学、生物学意义的遥感指标,以数据产品为导向发展多源协同遥感观测与分析处理方法,推动遥感从观测数据到数据产品的处理方法向标准化、结构化转变。

关键词: 遥感观测, 数据产品, 目标识别, 参数提取, 经验/半经验模型, 物理模型

Abstract:

This article takes remote sensing as one of measurements. The paper overviews the general methodology from remote sensing observations to data products, and categorizes the existing methods into two types: target recognition and parameter retrieval together with their features, advantages and shortcomings. Even after 50 years of continuous research, we are still lack of consistent and scientific methodology to produce data products from remote sensing observations. In the future, in order to build up scientific and structured remote sensing methods for data products, the priority should be given to further development of multi-angle, multi-temporal, multi-spectral, and multi-source as well as both active and passive remote sensing observations, so as to develop new remote sensing indices, which have obvious ecological, geographic, agronomic meanings, to promote the normalization and standardization of remote sensing methods, and to generate synthetic products based on all available remote sensing observations instead of single remote sensing observations. Big data and cloud computing will provide support for the process from remote sensing observations to data products.

Key words: remote sensing observations, remote sensing data products, target recognition, parameter retrieval, empirical/semi-empirical algorithm, physical model