地理科学进展 ›› 2010, Vol. 29 ›› Issue (5): 549-556.doi: 10.11820/dlkxjz.2010.05.006

• 资源与环境 • 上一篇    下一篇

内陆水体环境遥感监测研究评述

黄耀欢, 王 浩, 肖伟华, 秦大庸   

  1. 中国水利水电科学研究院 水资源研究所,北京 100038
  • 收稿日期:2009-08-01 修回日期:2010-03-01 出版日期:2010-05-25 发布日期:2010-05-25
  • 作者简介:黄耀欢(1982-),男,安徽黄山人,博士生,主要从事水文水资源、地理信息应用技术等方面的研究.E-mail: huangyh@lreis.ac.cn
  • 基金资助:

    国家自然科学基金创新研究群体科学基金项目(50721006);国家重点基础研究与发展计划(973)(2006CB403405).

The Review of Inland Water Environment Monitoring Based on Remote Sensing

HUANG Yaohuan, WANG Hao, XIAO Weihua, QIN Dayong   

  1. China Institute of Water Resources and Hydropower Research, Beijing 100038, China
  • Received:2009-08-01 Revised:2010-03-01 Online:2010-05-25 Published:2010-05-25

摘要:

应用遥感技术进行水质监测较常规监测具有巨大优势,能快速、大尺度、低成本的监测水质参数在时空上的变化状况.本文首先简要阐述了内陆水体环境遥感监测的基本原理;其次,介绍了常用水质参数的遥感监测研究现状,包括叶绿素a(Chl-a)、悬浮物(SS)、有色可溶性有机物(CDOM)以及总磷(TN)、总氮(TP)等其他水质参数,并将其划分为经验模型和生物光学模型两种监测方法;再次,对常用的遥感数据源进行了介绍和优缺点的评述;最后,讨论了内陆水体环境遥感监测误差来源,并对今后内陆水体环境遥感监测研究重点进行了展望.

关键词: 水体环境, 水质参数, 遥感监测

Abstract:

Using the technique of remote sensing to monitor the water quality is an advanced tool compared to traditional monitoring methods. This paper reviews the progress of application of remote sensing in inland water environment monitoring. Firstly, the principles of water environment parameters retrieval were addressed. Secondly, the applications of several inland water quality parameters in monitoring by remote sensing were discussed, including Chlorophyll a (Chl-a), Suspended Solids (SS), Colored Dissolved Organic Matter (CDOM), Total Phosphorus (TP), Total Nitrogen concentrations , transparency and turbidity of inland water and so on. And then we classified the existing retrieval models into empirical model and bio-optical model. Thirdly, different remote sensing images and their advantages and disadvantages for inner water environment monitoring were introduced. Finally, we discussed the problems of retrieval precision and proposed some suggestions for further research.

Key words: monitoring, remote sensing, water environment, water quality parameters