Remotely sensed greenness reflects vegetation biochemical traits and photosynthetic activity. This research focuses on the understudied pattern of decline of greenness within the growing season, known as the greendown effect, across the contiguous United States, investigating spatial patterns, biotic, and abiotic drivers of this greenness decline. In this study, code was developed to calculate greendown across a large region, then applied to two regions of interest, New York State and the terrestrial National Ecological Observatory Network (NEON) sites. The multivariate analyses revealed that temperature, vapor pressure deficit, and precipitation were consistently significant predictors of greendown variability, with temperature having the largest influence. Elevation and slope were significant for New York but not the NEON sites. Warmer temperatures were associated with steeper greendown slopes, possibly due to changes in leaf angle, consistent with previous studies. Vapor pressure deficit had conflicting effects between New York and the NEON sites, perhaps reflecting differing life history strategies across biomes. Greater precipitation led to shallower greendown slopes, reflecting the importance of water availability. Structural foliar traits, particularly leaf mass per area, were linked to shallower greendown slopes, indicating potential use of greendown as a remote sensing tool for forest health monitoring. The findings of this large-scale study quantifies seasonal greenness decline across major biomes in the contiguous United States for the first time and highlights the potential of using the greendown effect to infer canopy traits and ecosystem biodiversity.