We present a modeling approach to assess the robustness of remotely derived spectrometric equations predictive of rice leaf chlorophyll concentration to the view direction. Our methodology uses two radiative transfer models that operate at leaf（PROSPECT）and canopy（FCR）levels. It includes three stages:（1）Simulation canopy bi-directional reflectance on varies leaf chlorophyll concentration

varies leaf area index（LAI）and varies understory;（2）establishment of predictive relationships of chlorophyll concentration with stepwise regression;（3）assessment of the robustness of these relationships. First

we simulate rice leaf spectrum by PROSPECT model. The parameters used in the model are: protein concentration（0.0008g/cm2）

cellulose concentration（0.0049g/cm2）

water equivalent thick（0.02cm）and leaf structure parameter（1.7）

while chlorophyll concentration varises from 20μg/cm2 to 40μg/cm2

the change step is 0.1μg/cm2; simulate rice canopy bi-directional reflectance by FCR model. The parameters used in the model are: leaf relative linear size（0.40）

model inclination（81）

model eccentricity（0.9985）

sun zenith angle（32°）

sun azimuth angle（0°）

and sun direct radiation/total radiation（80%）

the changed parameters are LAI

view direction and understory spectrum character. LAI varies from 1 to 7. The change step is 1. There are four view directions: nadir view direction（both view zenith angle and azimuth angle equal to 0°）

hot spot view direction（view zenith angle equals to 32°

azimuth angle equals to 0°）

oblique view direction（view zenith angle equals to 32°

azimuth angle equals to 90°）and specular view direction（view zenith angle equals to 32° azimuth angle equals to 180°）. The reflectance of three types of understory are measured in 1999. Second

the established multiple linear regression model by stepwise regression analysis uses simulation values（LAI are 1

3

5 and 7

chlorophyll concentration varies from 20μg/cm2 to 40μg/cm2

three types of understory）on different directions to predict chlorophyll concentrations. The considered factors are canopy reflectance R λ

derived ln（1/R λ）and R′ λ. The regression equations are established by SPSS software. The selected wavelengths are upon F probability and root mean square error（RMSE）. Only when rejection probability is less than 0.05

the wavelength can be selected. To make the equation simple

we just chose three wavelengths which partial correlation index is the biggest for each regression equation. Then

the robustness is valued by compound coefficient of correlation（r2）and RMSE. For the equations establishment by the three factors（R λ

ln（1/R λ）

and R′ λ）

r2 on nadir view direction are 0.905

0.916 and 0.883

on hot spot view direction are 0.774

0.962 and 0.747

on oblique view direction are 0.563

0.941 and 0.572

on specular view direction are 0.881

0.937 and 0.883

RMSE on nadir direction are 1.97

1.68 and 1.99

on hot spot view direction are 2.75

1.13 and 2.92

on oblique view direction are 3.83

1.41 and 3.80

on specular view direction are 2.00

1.45 and 1.98. Those stress that the robustness are strongest on ln（1/R λ）and nadir direction. The equations on nadir direction are: chl=19.882+5747.957R 410-3770.780R 415-414.002R 705（1） chl=74.631-224.236ln（1/R 410）+190.960ln（1/R 415）+39.24ln（1/R 715）（2） chl=59.526-149370.7R′ 420+131394.74R′ 555+28899.908R′ 585（3） in the equations

chl indices chlorophyll concentration. We predict chlorophyll concentrations by nadir direction equations use simulated reflectance on other directions and see bigger RMSE occurred（for example

the biggest RSME is 26.74 on specular view direction）. It stresses that the equations built on nadir view direction are unsuitable for other directions’ prediction. When we predict chlorophyll concentrations by nadir direction equations use simulated reflectance on nadir direction（LAI equal to 2

4 and 6

chlorophyll concentration varies from 20μg/cm2 to 40μg/cm2

three types of understory）

we find that the predictive accuracy is