地理科学进展 ›› 2010, Vol. 29 ›› Issue (7): 803-810.doi: 10.11820/dlkxjz.2010.07.005

• 生态环境与灾害管理 • 上一篇    下一篇

湖冰遥感监测方法综述

魏秋方1,2, 叶庆华1,3   

  1. 1. 中国科学院青藏高原研究所,青藏高原环境变化与地表过程重点实验室, 北京100085|
    2. 中国科学院研究生院, 北京100049|
    3. 中国科学院遥感应用研究所, 遥感科学国家重点实验室, 北京100101
  • 收稿日期:2009-09-01 修回日期:2010-01-01 出版日期:2010-07-25 发布日期:2010-07-25
  • 作者简介:魏秋方(1983-),女,湖北随州人,自然地理学专业在读硕士,研究方向:资源环境遥感及其应用。E-mail:wei_qiufang@itpcas.ac.cn.
  • 基金资助:

    国家自然科学基金项目(40601056, 40121101); 国家重点基础研究发展计划项目(2009CB723901); 公益性行业(气象)科研专项(GYHY(QX)2007-6-18); 中国科学院遥感应用研究所遥感科学国家重点实验室开放基金项目; 中国科学院青藏高原研究所环境变化与地表过程实验室领域前沿项目。

Review of Lake Ice Monitoring by Remote Sensing

WEI Qiufang1,2, YE Qinghua1,3   

  1. 1. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, CAS,Beijing 100085, China|
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China|
    3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, CAS, Beijing 100101, China
  • Received:2009-09-01 Revised:2010-01-01 Online:2010-07-25 Published:2010-07-25

摘要:

本文综述了多光谱和微波数据监测湖冰冻结、消融及冰厚的方法,并比较了各种方法的优缺点,最后运用MODIS和AMSR-E监测了纳木错2007/2008冬半年冰情。湖冰监测方法主要有阈值法和指数法。阈值法是根据冰水反射率、 温度、后向散射系数等特征因子的不同直接区分冰水,精度较高,误差在5天以内。指数法主要是根据冰水波谱特性和极化特性,做波段运算后间接区分冰水。冰厚监测常采用经验公式法,用实测数据与反射率、极化比、亮温等建立关系式反演整个湖泊冰厚,此方法适用于特定的某个湖泊。冰厚识别是湖冰监测的难点,主动微波比多光谱数据更适合监测冰厚。从数据本身来讲,热红外、被动微波等高时间分辨率数据比可见光、主动微波等高空间分辨率影像更适合监测大面积湖泊冰情。基于多源遥感数据,发展自动反演算法将是湖冰遥感监测发展趋势之一。

关键词: 冰厚, 多光谱, 湖冰识别, 监测, 微波

Abstract:

This paper summarized and compared several methods of monitoring lake ice freezing-on and breaking up and ice thickness by multi-spectral and microwave remote sensing data. Finally, we monitored the lake ice in Nam Co by two methods during the winter half year of 2007/2008. Generally, researchers usually take threshold and index methods to monitor lake ice. According to the differences between ice and water, such as their reflectivity, temperature and backward scattering coefficients, the threshold model can distinguish ice and water directly. It has a high precision with an error of less than 5 days. While the index method recognizes ice and water indirectly by calculations based on spectral and polarization characteristics of ice and water. Additionally, researchers use empirical correlations between ice thickness and its reflectivity, polarization, temperature brightness or other properties to invert thickness. Ice thickness recognition is difficult in lake ice monitoring. Active microwave data is more suitable for ice thickness monitoring than multi-spectral data. Data with high time resolution such as thermal infrared and passive microwave data is more suitable for monitoring lake ice with large areas than the data with high spatial resolutions such as visible, near infrared and active microwave data. Based on multi-source remote sensing data, automatic inversion algorithm will be one of the development trends of lake ice monitoring by remote sensing.

Key words: ice thickness, lake ice recognition, microwave, monitoring, multispectral data