Vegetation indices (VIs) derived from multi-spectral imaging (MSI) can be used to collect non-destructive phenotypes that could be used to better understand development curves and interactions with environmental factors throughout the growing season. To investigate the amount of variation present in VIs derived from MSI and their relationship with important end-of-season traits, genetic and residual (co)variances for the VIs and their genetic and residual correlations with grain yield and grain moisture were estimated using maize data collected as part of the Genomes to Fields (G2F) initiative. One of the VIs considered in this study was normalized difference vegetation index (NDVI). In addition to NDVI, cumulative NDVI (cNDVI) was used as a phenotype to explore methods to simultaneously fit multiple phenotypes from MSI collected throughout the growing season. The potential of random regression models were investigated using either linear Splines or Legendre polynomial functions. Low to moderately high heritability estimates (0.10 – 0.35) was observed for NDVI values at each of the time points within years, indicating that there exists a reasonable amount of genetic variation. Moreover, strong genetic and residual correlations were found between grain yield and NDVI. Finally, it was found that using random regression with either of the functions converged using all time points and show a potential to be used as an alternative to multi-trait models.