Cotton is an indeterminate crop; therefore, fertility management has a major impact on the growth pattern and subsequent yield. Remote sensing has become a promising method of assessing in-season cotton N status in recent years with the adoption of reliable low-cost unmanned aerial vehicles (UAVs), high-resolution sensors and availability of advanced image processing software into the precision agriculture field. This study was conducted on a UGA Tifton campus farm located in Tifton, GA. The main goal of this study was to correlate in-season cotton N status with multispectral imagery acquired with a UAV. For this study, six N treatments consisting of 0, 34, 67, 101, 135 and 168 kg/ha rates were applied to attain varying levels of plant N status within the same field. Cotton tissue samples were collected during crop growth stages (first, third, fifth, and seventh week of bloom) to quantify plant N status during these stages. Tissue analysis results provided leaf blade N (%) and petiole N (ppm) for each N treatment implemented in the study. Crop multispectral imagery in the spectral wavelengths of 550 nm (green), 660 nm (red), 735 nm (red-edge) and 790 nm (near infrared) was acquired during these crop growth stages by utilizing a commercially available quadcopter equipped with a high-resolution multispectral camera. Different vegetation indices (VIs) were selected and calculated based on potential correlation with plant N status and were calculated from the data acquired from the multispectral aerial imagery. Correlations between the indices and leaf blade N (%) and petiole N (ppm) as obtained from plant tissue analysis were compared. Regression equations correlating the VIs to actual N levels were generated to evaluate the use of different VIs for accurately measuring N levels in the crop at the selected growth stages. Initial data analysis indicated that NDVI was strongly correlated to leaf blade N (%) and petiole (ppm) from the first week of bloom samples, whereas, NDRE had stronger correlation for the samples that were taken in the third, fifth, and seventh week of bloom. These correlations may provide promise for using multispectral imaging to detect in-season N variability in cotton.