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Composite scene of row crops induced an unavoidable error in ground measurements of directional brightness temperature (DBT) due to the use of wide field of view (FOV). The measurement results vary with sample size and position, detector height and view direction, and bias due to project principle. This is called FOV effect. The study focuses on the estimation of FOV effect on the measurements of maize canopy using a computational geometric 2D model. The model was developed to simulate the fractional variations of canopy brightness temperature components. The simulation results revealed that the errors caused by FOV effect have a complex feature. Generally, vegetation fraction is always over counted in the nadir view, errors increase dramatically with the decrease of detector height as well as the enlargement of sample size, the deviation of the error corresponding to detect position is small; in oblique view, the errors are limited to a low level due to an effect called compensation effect. However, the deviation of the error keeps large when the sample size is small. Nevertheless, the best approach to reduce FOV effect in ground observation is levering the detector to a higher altitude as the model suggested.