Abstract: Abstract: In recent years, remote sensing images obtained by different types of optical sensors from a ground platform are applied to crop-weed discrimination and serve variable-rate technology in precision agriculture. Classification accuracy in remote sensing is influenced by spatial scale, so choosing the optimal spatial scale can be helpful for field data acquisition. Influences of spatial scale on classification accuracy in remote sensing are mainly originated from two factors: one factor is mixed-pixel and the other factor is spectral variability. Both aggravated mixed pixel caused by a larger spatial scale and aggravated spectral variability caused by a smaller spatial scale will result in classification accuracy reduction in remote sensing. For geographic entities in remote sensing images have inherent spatial attribute and spectral attribute, a spatial scale exists which can minimize the net effect of both mixed-pixel and spectral variability. Under this spatial scale, pixels can have optimal spectral identifiability. An approach for the selection of optimal spatial scale using a spectral angle mapper to measure the net effect of both mixed-pixel and spectral variability was proposed for crop-weed discrimination. The basic thinking of optimal spatial scale selection based on spectral angle mapper is as follows: using the average spectra calculated from a great amount of pure pixels belonging to one kind of ground object as the reference spectra for this kind of ground object, the spectra of each pixel could be regarded as the sum of its reference spectra and the net effect of mixed-pixel and spectral variability. Then, the spectral angle between the pixel spectra under different spatial resolutions and its reference spectra might be calculated to measure the net effect of mixed-pixel and spectral variability. The pixel will have optimal spectral identifiability when the net effect is least, and in this case, the spatial scale is the optimal scale. The proposed approach was realized in one field image. The geographic entities in the image were objectified. The optimal spatial scale was 0.48 cm by using the spatial scale selection method based on a spectral angle mapper. The relationship between the area and shape indexes of the target object and its optimal spatial scale was analyzed theoretically. For other field scenes, the finding can provide a reference for optimal spatial scale selection by calculating the area and shape indexes of plant objects.