Low-altitude hyperspectral observation systems are promising sensing tools for acqui- sition of optical remote-sensing data under the humid subtropical climate in Japan. The system is also capable of acquiring leaf-scale optical information free from atmo- spheric effect. However, the leaf-scale hyperspectral data are affected by shading and various illumination conditions such that it is difficult to obtain consistent character- istics of the spectral information. The aim of this article is the extraction of Lambert coefficients as an inherent leaf spectral profile. In this work, we propose a dichromatic model-based principal component analysis on hyperspectral data by utilizing leaf-scale hyperspectral data in order to diminish the spectral difference caused by the illumina- tion condition and bidirectional reflectance distribution function. The results show that indices of chlorophyll content based on the estimated Lambert coefficients are consis- tent with the growth stages of a paddy field, whether the illumination condition is clear sky or overcast.
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