Digital aerial imagery (DAI) of the crop canopy collected by aircraft and unmanned aerial vehicles is the yardstick of precision agriculture.  However, the quantitative use of this imagery is often limited by its variable characteristics, low quality, and lack of radiometric calibration.  To increase the quality and utility of using DAI in crop management, it is important to evaluate and address these limitations of DAI.  Even though there have been improvements in spatial resolution and ease of imagery access, current DAI sources appear to lack the end-user demand for products that provide more than just an aesthetic image of a place-specific snapshot at a given time.  The objective of this study was to establish a site to test quality and different methods for radiometric calibration of DAI over time.  A 120-ha study area located in Story County, Iowa was used during the 2015 growing season.  Commercial calibration tarps with known reflectance (3, 6, 12, 22, 44, and 56%) values were deployed in a study area with two fields of corn (Zea mays L.) and two fields of soybean (Glycine max L.).  Two commercial DAI providers collected 0.2 meter and 0.5 meter, multi-spectral (blue, green, red, and NIR) digital imagery every 10 to 12 days throughout the growing season.  Empirical line method and segmental linear regression calibration techniques were utilized to convert digital numbers (DNs) to percent reflectance, which were used to create standardized NDVIs that could be compared across time (date to date) and across space (field to field).  Image processing challenges resulted from a highly non-linear mathematical relationship between DN values and percent reflectance, and from significant inaccuracies in the spatial registration of pixels from flight to flight.  Even so, calibrated temporal vegetation indices were produced allowing for identification of agronomic areas that had similar vegetative health over time.  The analyses showed that it is critical for DAI providers to maintain the purest form of the original digital values (with minimal post-processing manipulation) to allow for radiometric calibration of the data for use in spatiotemporal vegetation indices, crop modeling, and any other standardized image comparisons for use in crop management.