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五种TM影像大气校正模型在植被遥感中的应用

宋巍巍;管东生   

  1. 中山大学环境科学与工程学院, 广州 510275
  • 收稿日期:2007-01-29 修回日期:1900-01-01 出版日期:2008-04-20 发布日期:2008-04-20

Application of five atmospheric correction models for Landsat TM data in vegetation remote sensing.

SONG Wei-wei;GUAN Dong-sheng   

  1. School of Environmental Science and Engineering, Sun Yatsen University, Guangzhou 510275, China
  • Received:2007-01-29 Revised:1900-01-01 Online:2008-04-20 Published:2008-04-20

摘要: 基于2005年7月18日广州市东北部和惠州市北部的TM影像,以表观反射率模型为参照,从植被反射率光谱、地物反射率统计特征、规一化植被指数三方面对4种黑体减法模型和6S模型在植被遥感中的应用进行了评价.结果表明:黑体减法模型DOS4获得了精度较高的植被反射率,其地物反射率与规一化植被指数的信息量最大,适用于研究区的植被遥感研究.对于不同区域的植被遥感研究需要进行具体的比较分析,才能选择到合适的大气校正模型.

关键词: Logistic, 海湾型城市, 土地利用, 生态系统服务, CA-Markov, 情景模拟

Abstract: Based on the Landsat TM image of northeast Guangzhou City and north Huizhou City on July 18, 2005, and compared with apparent reflectance model, five atmospheric correction models including four dark object subtraction models and 6S model were evaluated from the aspects of vegetation reflectance, surface reflectance, and normalized difference vegetation index (NDVI). The results showed that the dark object subtraction model DOS4 produced the highest accurate vegetation reflectance, and had the largest information loads for surface reflectance and NDVI, being the best for the atmospheric correction in the study areas. It was necessary to analyze and to compare different models to find out an appropriate model for atmospheric correction in the study of other areas.

Key words: Logistic, bay city, land use, ecosystem service, CA-Markov, scenarios simulation