Calculating environmental moisture for per-field discrimination of rice crops

Abstract

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.