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Mastering the changes in the planting area of crops are critical for formulating sustainable agricultural development strategies. The Landsat 9 satellite provides continuous observational data for crop mapping. It is necessary to evaluate the potential of Landsat 9 images for canola mapping. According to literatures, some canola indices have been proposed in relation to the spectral features of the yellow canola flower, such as the canola ratio index (CRI), normalised difference yellowness index (NDYI), canola index (CI), canola flower index (CFI), normalised difference rapeseed flower index (NRFI) and winter rapeseed index (WRI). However, it is unclear which index has more potential to extract canola based on the Landsat 9 images. Therefore, the potentials of these indices for canola mapping were quantitatively compared by using different classification methods including supervised and unsupervised classification methods. For supervised classification methods and the CFI, the overall accuracy of canola mapping was above 90% and the kappa coefficient was above 0.8. For unsupervised classification methods, CFI also performs best. It was demonstrated that the CFI outperformed the other five indices for canola mapping. It was also confirmed that Landsat 9 images and the CFI exhibit promising potential for canola mapping via quantitative comparison.