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15 October 2015 Using hyperspectral data for rice LAI estimation at different phenological periods
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With the development of hyperspectral remote sensing technology, there are more and more researches which are related to monitoring the growth condition of rice by it. However, most of recent researches focus on the biochemical component content by monitoring hyperspectral of rice leaf. As a consequence, there are rare researches which estimate rice leaf area index by analyzing canopy hyperspectral feature at different phenological periods. After field investigation, we find that from tillering to jointing, the rice’s canopy structure changed obviously and LAI increased fast. The situation of rice’s growth at this stage has an incredible influence on its late growth and yield. After jointing stage, the change tendency of LAI tends to be steady and the characteristic change of canopy structure is unapparent. If we get hyperspectral of rice’s canopy at the right time, we can analyze the characteristics and predict the tendency of canopy. It’s also valuable on guiding the management of rice field. On the other hand, this paper also gives useful reference on crop condition monitoring using hyperspectral. For all this, using ASD and LAI-2000 to measure rice canopy spectral reflectance and LAI in tillering and jointing stage. Then the relationship between spectral reflectance and LAI is analyzed in two periods. In order to quantitatively describe the correlation, the relationship between red edge parameters and LAI is studied and rice LAI estimation model is build. Finally, using measured data to evaluate this model. The results show that using hyperspectral feature of rice to estimate LAI is feasible.
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Yan-jun Yang and Qing-jiu Tian "Using hyperspectral data for rice LAI estimation at different phenological periods ", Proc. SPIE 9672, AOPC 2015: Advanced Display Technology; and Micro/Nano Optical Imaging Technologies and Applications, 96720N (15 October 2015);

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