We explore the feasibility of detecting nitrogen deficiencies in a cornfield using exclusively multispectral satellite imagery. Attempts to leverage Sentinel-1 and Sentinel-2 data demonstrate that while nutrient shortages are successfully identified through multi-sensor approaches— often incorporating UAV imagery, ground-based measurements, or advanced vegetation indices—reliably detecting these deficits from satellite imagery alone remains challenging. Data scarcity, uneven initial and structural soil conditions, noise introduced by atmospheric effects and cloud cover complicate the direct association of spectral signals with nutrient levels. Despite observed differences in key vegetation indices, such as NDVI, between fertilized and unfertilized plots at late growth stages, early-season detection proved unreliable. These findings illustrate the complexity of detecting nutrient deficiencies and highlight the need for improved data quality, supplementary ground truth measurements, or more sophisticated modeling approaches to enhance the accuracy and accessibility of satellite-only nutrient deficiency detection in precision agriculture.