Accurately quantifying foliage inputs to soil carbon budget is crucial for understanding soil development but is challenging due to forest type variability and extensive field work. Despite several foliar biomass estimation methods, no singular relationship exists between foliar biomass and leaf-fall. My research aims to establish empirical relationships between foliar biomass and leaf-fall to evaluate inputs to soil carbon storage across temperate forest and explore the scope of remote sensing for these estimates. Our study site includes 46 plots in Borden Forest near Angus, Ontario, within mixed-wood plains ecoregion. It features two ~100-year-old stands on former pastureland: a naturally regenerated deciduous-conifer mix and a dual-aged red and white pine plantation. Leaf fall was collected in the autumn of 2024 and separated into needles, leaves, and woody debris. Foliar biomass was estimated in the preceding summer using allometric equations based on tree species, height, and diameter at breast height, then compared with the leaf-fall data. I assess the influence of methodological errors in the two field sampling techniques for estimating annual leaf-fall. I hope to improve foliar biomass estimates using optical remote sensing and 3D structural LiDAR data. Finally, I evaluate methodological errors in both remote sensing and field sampling for annual leaf-fall estimation. The study aims to reveal strong foliar biomass and leaf-fall relationship across forest types and demonstrate the potential for extending our understanding of soil carbon accumulation mechanism using remote sensing, thereby reducing extensive fieldwork. We hope to further define coniferous foliage turnover rates and highlight differences in soil development under two distinct management strategies.