利用统计和物理模型反演植物生化组分的比较

doi:10.7523/j.issn.2095-6134.2004.1.012

中国科学院大学学报 ›› 2004, Vol. 21 ›› Issue (1): 78-83.DOI: 10.7523/j.issn.2095-6134.2004.1.012

利用统计和物理模型反演植物生化组分的比较

阮伟利, 牛铮

中国科学院遥感应用研究所遥感信息科学重点实验室, 北京 100101

收稿日期:2003-04-21 修回日期:2003-05-26 发布日期:2004-01-10
基金资助:
国家自然科学基金(40271086);国家重点基础研究发展规划项目(G2000077900);中国科学院知识创新工程重大项目(KZCX1SW01;KZCX2312)资助

Comparison of Retrieving Plant Biochemical Components with Statistical and Physical Models

Ruan Wei-li, Niu Zheng

Key Laboratory of Remote Sensing Information Sciences, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China

Received:2003-04-21 Revised:2003-05-26 Published:2004-01-10

摘要/Abstract

摘要：

利用不同形式的光谱数据,如反射率、透射率和吸收率,以及经过不同波段间隔和噪声水平处理的反射率,直接比较了统计模型和物理模型反演鲜叶片叶绿素、水和干物质含量的效果。结果表明,物理模型对光谱数据波段间隔和噪声的鲁棒性比统计模型要好,而在反演的绝对效果上,统计模型的反演结果特别是对水分的反演与物理模型相当,要充分考虑统计模型和物理模型反演植物生化组分时的优缺点,提高反演的精度和效率。

关键词: 统计模型, 物理模型, 植物生化组分, 成像光谱遥感

Abstract:

Using reflectance,transmittance,absorptance and reflectance of different spectral interval and disturbed by noises,comparison is made between the effect of retrieving foliar biochemical components with statistical models and that with physical models. The result shows that the physical model is more robust to spectral interval and noises than the statistical model. However,as far as the real retrieving values are concerned,especially when it comes to the inversion of water in fresh leaves,the statistical model is as good as the physical model. So in order to improve precision and efficiency in retrieving plant biochemical components,statistical and physical models should be used according to the purpose of research.

Key words: statistical model, physical model, plant biochemical component, hyperspectral remote sensing

中图分类号:

阮伟利, 牛铮. 利用统计和物理模型反演植物生化组分的比较[J]. 中国科学院大学学报, 2004, 21(1): 78-83.

Ruan Wei-li, Niu Zheng. Comparison of Retrieving Plant Biochemical Components with Statistical and Physical Models[J]. , 2004, 21(1): 78-83.

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利用统计和物理模型反演植物生化组分的比较

Comparison of Retrieving Plant Biochemical Components with Statistical and Physical Models

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