UAV-Based Spectral Indices Reveal Species-Specific Yield Relationships in Spring Cereals Across Contrasting Seasons

Climate variability and recurrent spring drought increasingly affect cereal production in Central Europe, creating a need for field phenotyping methods that can detect crop responses before harvest. This study evaluated UAV-derived multispectral vegetation indices in relation to grain-yield variation in spring wheat, spring barley, and oat grown at the same location during two climatically contrasting seasons. Multispectral measurements were obtained repeatedly during crop development in drought-affected 2024, characterized by faster thermal accumulation, and in the wetter and cooler 2025 season. Spectral–yield relationships differed markedly between seasons and species. In 2024, correlations with yield were strong in wheat (up to r = 0.793) and barley (up to r = 0.826), but moderate in oat (up to r = 0.512). In 2025, these relationships were weaker in wheat and barley and non-significant in oat. After adjustment for field-strip effects, this seasonal contrast remained in wheat and barley, although with weaker correlations, but not in oat: the 2024 oat correlations disappeared, whereas selected 2025 correlations became significant. PCA showed that vegetation indices formed a common spectral gradient, while the association between grain yield and this gradient varied by species and season. CART analysis supported these patterns, with more complex trees and stronger cross-validated performance in 2024. The results show that UAV spectral phenotyping captured yield-related canopy variation more clearly in 2024, and that the informative spectral traits and their relationships with yield differed among species and between the two contrasting seasons.