This study aimed to evaluate the relationship of 12 vegetation indices (VIs) derived from satellite imagery with field-measured Normalized Difference Vegetation Index (NDVI) in cotton, with particular emphasis on measurement agreement, phenology-dependent variation, and shared versus complementary information content. In this context, Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Enhanced Vegetation Index 2 (EVI 2), Modified Triangular Vegetation Index 2 (MTVI2), Modified Normalized Difference Vegetation Index (MNDVI), Moisture Stress Index (MSI), Normalized Difference Moisture Index (NDMI), Ratio Drought Index (RDI), Modified Red-Edge Normalized Difference Vegetation Index (MRENDVI), Structure Insensitive Pigment Index (SIPI), Soil-Adjusted Vegetation Index (SAVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) derived from Sentinel-2 imagery were compared with NDVI measurements obtained using a GreenSeeker handheld sensor across different phenological stages. Relationships were examined using regression analysis, Bland–Altman analysis, and principal component analysis (PCA). Models incorporating phenological stage effects showed that the index–NDVI relationships were generally strong at the seasonal scale (R² = 0.848–0.867), but varied according to both index type and phenological stage. Stage-wise analyses further indicated that the strongest relationships were consistently observed in the early season, whereas explanatory power declined during the mid- and especially late-season. MTVI2, MSAVI, ARVI, and GARI exhibited patterns more similar to NDVI in tracking canopy development, whereas NDMI, MSI, RDI, MRENDVI, SIPI, and partly EVI2 reflected distinct sensitivities related to water status, pigment composition, and physiological change. These findings suggest that the tested indices should not be interpreted in terms of single-index superiority, but rather in terms of overlapping and complementary information under different phenological and biophysical conditions. Overall, for vegetation monitoring in cotton, a multi-index framework selected according to phenological stage and monitoring objective appears more appropriate than a single-index approach.