Skip to main content

Estimation of Lambert parameter based on leaf-scale hyperspectral images using dichromatic model-based PCA

Buy Article:

$60.90 plus tax (Refund Policy)


Low-altitude hyperspectral observation systems are promising sensing tools for acquisition 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 atmospheric effect. However, the leaf-scale hyperspectral data are affected by shading and various illumination conditions such that it is difficult to obtain consistent characteristics 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 illumination condition and bidirectional reflectance distribution function. The results show that indices of chlorophyll content based on the estimated Lambert coefficients are consistent with the growth stages of a paddy field, whether the illumination condition is clear sky or overcast.

Document Type: Research Article


Affiliations: Interdisciplinary Graduate School of Science and Engineering,Tokyo Institute of Technology, Yokohama, Japan

Publication date: February 20, 2013

More about this publication?

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more