Phytonematodes are parasites of plant tissues, primarily roots. Although they cause significant damage to agriculture, they are still underexplored. Soybean crops suffer significant yield losses due to phytonematode infestations. Remote sensing techniques offer an effective means of detecting and analyzing these infestations. This study aimed to identify the most suitable stage in the soybean plant's growth cycle for detecting spectral differences between nematode-infested and healthy plants using MSI/Sentinel-2 imagery. The study utilized three sets of Sentinel-2 images, representing different growth stages (December - vegetative, January - grain development, February - maturation). 38 nematological sample points were classified into three infestation levels (no or low, moderate, high). Spectral responses were collected for each growth stage, followed by an analysis of reflectance values and NDVI to assess class separability. The maturation stage exhibited the most significant spectral differences between infestation levels, especially in the near-infrared and red-edge regions. The sum of squared differences demonstrated the greatest separability in the maturation stage, particularly between moderate and no or low infestation categories. The results suggest that the end of the reproductive stage is optimal for detecting spectral differences in soybean plants affected by nematodes.