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中国科学院大学学报 ›› 2015, Vol. 32 ›› Issue (1): 63-69.DOI: 10.7523/j.issn.2095-6134.2015.01.011

• 环境科学与地理学 • 上一篇    下一篇

微波遥感定量反演研究中大气参数的估算模型

周芳成1, 宋小宁1, 李召良2, 马建威3   

  1. 1. 中国科学院大学资源与环境学院, 北京 100049;
    2. 中国农科院农业资源与农业区划研究所, 北京 100081;
    3. 中国水利水电科学研究院, 北京 100044
  • 收稿日期:2014-02-25 修回日期:2014-03-14 发布日期:2015-01-15
  • 通讯作者: 宋小宁
  • 基金资助:

    国家自然科学基金(41231170)和中国科学院西部行动计划项目(KZCX2-XB3-15)资助

Estimation model of the atmospheric parameters in the research of quantitative microwave remote sensing

ZHOU Fangcheng1, SONG Xiaoning1, LI Zhaoliang2, MA Jianwei3   

  1. 1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China;
    3. China Institute of Water Resources and Hydropower Research, Beijing 100044, China
  • Received:2014-02-25 Revised:2014-03-14 Published:2015-01-15

摘要:

被动微波遥感对地表参数的反演研究中通常会忽略大气的影响.在定量遥感精度要求较高的情况下,建立大气参数的定量估算模型以剔除大气影响,对于提高反演精度具有积极作用.本文基于辐射传输模型,总结出大气向上和向下亮温及大气透过率这3个大气参数均与大气可降水量密切相关.利用MonoRTM模型和全球946条大气廓线建立晴空条件下大气亮温和大气透过率与大气可降水量之间的函数关系,发现大气亮温与大气可降水量呈正相关,大气透过率与大气可降水量呈负相关.本文利用中国张掖地区探空数据集对大气参数估算模型进行验证.模拟结果与通过MonoRTM模拟的结果基本一致,表明本模型具有较高的可靠性.通过本模型可定量计算大气对被动微波遥感的影响,提高微波遥感定量反演的精度.

关键词: 微波遥感, 大气亮温, 大气透过率, 大气可降水量

Abstract:

Usually, atmospheric effects on microwave remote sensing are ignored because of the penetration of microwave. However, for quantitative inversion in which high precision inversion is required, atmospheric effects should be taken into account. Based on the radiative transfer model, three atmospheric parameters, upwelling and downwelling atmospheric brightness temperature and atmospheric transmissivity, were proved to be much influenced by precipitable water vapor. The MonoRTM and 946 global atmospheric profiles were used to simulate these atmospheric parameters. Then we got regression models for PWV(precipitable water vapor) and atmospheric brightness temperatures and for PWV and atmospheric transmissivity, respectively. Finally, Zhangye radiosonde data sets were used to validate the models, and verification results showed that the estimation models had high credibility.

Key words: microwave remote sensing, atmospheric brightness temperature, atmospheric transmissivity, precipitable water vapor

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