首页> 外文会议>Workshop on Hyperspectral Image and Signal Processing >DECOMPOSING THE CONTRIBUTION OF FOLIAR NITROGEN CONTENT AND CANOPY STRUCTURAL PROPERTIES IN THE REFLECTANCE SPECTRA OF CEREAL CROPS
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DECOMPOSING THE CONTRIBUTION OF FOLIAR NITROGEN CONTENT AND CANOPY STRUCTURAL PROPERTIES IN THE REFLECTANCE SPECTRA OF CEREAL CROPS

机译:分解叶面氮含量和冠层结构性质在谷类作物的反射光谱中的贡献

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Recent studies have debated over the effect of canopy structure on the remote sensing of foliar nitrogen content. Although previous work reported on the radiative transfer modeling of canopy structural influence, the modeling implementation is still of limited use in the hyperspectral community. This study proposes to use a multi-scale tool, continuous wavelet analysis, to decompose the spectral responses to variations in canopy structure and foliar nitrogen content. Our results demonstrated that the leaf area index (LAI) and leaf nitrogen content (LNC) were best correlated to the wavelet features (732 nm, scale 6) and (735 nm, scale 4), respectively. These two wavelet features could be used to separate the spectral contributions of LAI and LNC at different scales. The findings provide a new perspective for understanding the effect of canopy structure on hyperspectral remote sensing of foliar nitrogen.
机译:最近的研究对冠层结构对叶面氮含量遥感的影响进行了争论。虽然以前的工作报告了冠层结构影响的辐射转移建模,但建模实施仍然有限使用了高光谱群落。本研究提出使用多尺度工具,连续小波分析,分解对冠层结构和叶状氮含量的变化的光谱响应。我们的研究结果表明,叶面积指数(LAI)和叶片氮含量(LNC)分别最佳地与小波特征(732nm,3)和(735nm,4)。这两个小波特征可用于将LAI和LNC的光谱贡献分离在不同的尺度。该研究结果提供了一种新的视角,了解冠层结构对叶状氮的高光谱遥感的影响。

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