The accuracies of rice classifications determined from density slices of broadband moisture indices were compared to results from a standard supervised technique using six reflective Enhanced Thematic Mapper plus (ETM+) bands. Index-based methods resulted in higher accuracies early in the growing season when background moisture differences were at a maximum. Analysis of depth of ETM+ band 5 resulted in the highest accuracy over the growing season (97.74%). This was more accurate than the highest supervised classification accuracy (95.81%), demonstrating the usefulness of spectral feature selection of moisture for classifying rice.
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Niel et al. (2003) studied this question.