Spectral imagery using micro-unmanned aerial vehicles is rapidly advancing. This study compared reflectance calibration methods for imagery acquired using the Parrot Sequoia imager, a commercial multispectral sensor package. For the study, two orthomosaics were calibrated using 1) a manufacturer-suggested AIRINOV standard correction using PIX4D software and 2) the Empirical Line Calibration (ELC) method using ground radiometric data on specific in-scene targets. Both scenes were analyzed for target spectral agreement by ground radiometric survey. Regression analysis demonstrated more favorable target correlation for the ELC imagery than the AIRINOV-calibrated imagery, with Root Mean Square Error (RMSE) analysis supporting these results. Finally, classification maps were produced between the data sets. Error analysis resulted in an overall accuracy of 24% for the AIRINOV map compared to ELC-based truth data, with a considerable number of pixels associated with brighter targets unclassified. These results demonstrate the need for standardized calibration procedures in the spectral correction of small-format remote sensor data.