Leaf Parameter Estimation Based on Leaf Scale Hyperspectral Imagery

Low altitude hyperspectral observation systems provide us with leaf scale optical properties which are not affected by the atmospheric absorption and spectral mixing due to the long distance between the sensors and objects. However, it is difficult to acquire Lambert coefficients as inherent leaf properties because of the shading distribution in leaf scale hyperspectral images. In this paper, we propose an estimation method of Lambert coefficients by making good use of the shading distribution. The surface reflection of a set of leaves is modeled by a combination of dichromatic reflection under direct sunlight and reflection under the shadow of leaves. It is shown that hyperspectral distribution of leaves is composed of three linear clusters, i.e., specular, diffuse and shadowed clusters. Lambert coefficient is derived from the first eigenvector of diffuse cluster. Experimental results show that chlorophyll indices based on the estimated Lambert coefficients are consistent with the growth stages of paddy fields.