Active sensors, mounted on typical agricultural equipment, can be used to estimate N (nitrogen) status in corn (Zea mays L.) on a sub-field scale. This gives a producer the potential to improve N fertilizer recommendations for crop production and potentially reduce nitrate loss to the environment. This study examines the relationship between crop canopy reflectance data and yield in corn fields following corn, soybeans, alfalfa, and soybeans (with manure history) in Central Pennsylvania. Pre-plant whole-plot treatments included a control, 56 kg N ha-1 as NH4NO3, and 129-185 kg N ha-1 as manure. Split-plot treatments included six sidedress rates (0, 22, 45, 90, 135, and 180 kg N ha-1) and one pre-plant rate (280 kg N ha-1) as NH4NO3 in 9.1 x 4.5 m plots. Georeferenced canopy reflectance data in the 590nm and 880nm wavelengths were taken each week from early May until mid-July. This data was used to calculate the Normalized Difference Vegetation Index (NDVI) for each plot. Preliminary results using Cate-Nelson indicate that the NDVI was able to accurately separate the data into responsive and non-responsive populations. Further examination of the data from the limited number of responsive sites indicates that there is a general relationship between NDVI and economic optimum N rate (EONR). Results from the sensor were also similar to those from a SPAD meter, suggesting that previous response studies using the SPAD meter could be used to develop N recommendations based on the sensor. These results suggest that employing a ground-based active sensor appears to be a viable option for predicting corn response to sidedress N and may provide useful information for recommending sidedress N rates; however, more data in the responsive range is needed to further develop this relationship.