Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.