There are nonlinear relations among the bi directional reflectance of rice

its canopy architecture parameters

the spectral characteristics of the different components of rice

and the illumination and viewing geometry. This article explores the use of artificial neural network for both forward and inverse bi directional reflectance modeling of rice based on the data measured in Zhejiang University（Hangzhou

China） for field experiments from 1999 to 2000. The assumption here is that the bi directional reflectance of a rice canopy is the function of the geometry of its constituent elements

the spatial distribution

spectral features of the elements

and the illumination and viewing geometry. This implies that the bi directional reflectance of the canopy is particular sensitive to the canopy’s structural parameters

the spectral characteristic of foliage

and the illumination and viewing direction. It also implies that canopies with different parameters will exhibit different bi directional reflectance. On the basis of these analysis

we decided to have 10 input parameters:model inclination angle of the canopy（ θ ）

eccentricity（ D ）

reflectance of foliage（ R ）

transmittance（ T 1 ）

sun zenith angle（ θ s ）

soil reflectance（ sbrf ）

the ratio of mean length to canopy height（ L 1）and the ratio of width to length（ P ）

leaf area index（ LAI ）

diffuse to total incident radiation（ Q 1）. There are 17 output parameters:bi directional reflectance of the canopy in the principal plane

from -60° in the forescattering direction to +60° in the backscattering direction at increments of 7 5° in forward BP model. On the other hand

there are 3 output parameters:leaf area index

the ratio of mean length to canopy height and the ratio of width to length

and other 22 parameters mentioned above except eccentricity and model inclination angle of the canopy are input parameters in inverse BP model. After model development

the neural network model is tested against the independent data set. The Root mean square error between the bi directional reflectance of rice measured and simulated varies from 4 53×10 -6 to 3 67×10 -3 . The inversion model of artificial neural network is able to inverse the rice canopy structural parameters with 81 8% accuracy. The results of both forward and inverse modeling suggest that the model of artificial neural network is of high precise to simulate the relations of the bi directional reflectance of rice and its canopy structural parameters. Further research is needed to monitor the rice growth by the neural network model.