The small-unmanned aircraft vehicles (sUAVS) are currently gaining more popularity in agriculture with uses including identification of weeds and crop production issues, diagnosing nutrient deficiencies, detection of chemical drift, scouting for pests, identification of biotic or abiotic stresses, and prediction of biomass and yield. Research information on the use of sUAVS have been published and conducted in crops such as rice, wheat, and corn, but the development of support tools associated with this technology is still under progress. One main example on the use of the sUAVS technology is portrayed herein for winter canola. Research studies were performed in Manhattan, Kansas at the Kansas State University –Department of Agronomy-, in a joint effort pursued by the Crop Production and Ecology and Agriculture Spatial Analysis Laboratory groups. Winter canola acres are gradually rising in the southern Great Plains region. Optimum nutrient management, specifically related to the right rate of nutrient to be applied, should be pursued to maximize crop production. The main issue faced by the scientific community is that the lack of information about nutrient management for canola. This study has as a main objective to: 1) determine biomass and nutrient accumulation for winter canola; 2) establish correlations between these parameters and blue NDVI and canopy temperature; and 3) determine the predictable value of blue NDVI and canopy temperature in assessing crop production issues and final canola yield. At flowering, blue NDVI presented a high correlation in predicting the whole-plant biomass under diverse mass levels. As expected, the canopy temperature map collected via sUAVS showed a trade-off relationship with whole-plant biomass, suggesting an optimum plant temperature value for maximizing solar radiation capture and efficiency in conversion (measured as final biomass). Both blue NDVI and canopy temperature, determined by the use of the sUAVS, predicted very well biomass status on canola. This information might help guide future nutrient prescriptions at the site-specific level. For the future, preparation of support decision tools are needed in order to quantify the “real” contribution of this technology in assisting key stake-holders for facilitating the decision-making process.