Composite scene of row crops induced an unavoidable error in ground observations due to the use of wide field of view （FOV） in the measurements. The measurements vary with sample size and position

detector height and view direction

and bias due to project principle

which is called FOV effect. This study focused on the estimation of FOV effect on the measurements of maize canopy directional brightness temperature （DBT） using a computational geometric 2D model. The model was developed to simulate the fractional variations of canopy brightness temperature components. In this research

the maize canopy was classified into three brightness temperature components: sunlit soil

shaded soil and vegetation

each component has a unique brightness temperature value. The simulation results revealed that the errors caused by wide FOV have complex features due to canopy geometry and measurement geometry. Generally

vegetation fraction is always over counted in the nadir

errors increase dramatically with the decrease of detector height as well as the enlargement of sample size

and the deviation of the error corresponding to sample position is small. In oblique view

the errors are limited to a low level due to the compensation effect. The best approach to reduce this kind of error is to set the detector to a higher altitude as the model suggested.