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Grasslands offer a sustainable and cost-effective resource for livestock feed while supporting carbon sequestration, thereby mitigating climate change. However, current remote sensing methods for grassland monitoring have not adequately addressed adaptive grazing management at the fine scales required for intensive grazing systems. Grazing trials conducted under La Niña conditions, with recovery periods of 3, 6, 9, 12, and 15 months, aimed to advance regenerative grazing techniques in small plots (<1 hectare). Sentinel-2 imagery combined with random forest outperformed XGBoost in estimating biomass, achieving better regression statistics (R² = 0.56, RMSE = 1,532 kg DM/ha vs. R² = 0.48, RMSE = 1,726 kg DM/ha). The model effectively captured carbon stock variability across recovery periods, with the 3-month recovery exceeding 2,000 kg C/ha. This proof-of-concept study underscores the potential of high-resolution remote sensing to enhance precision agricultural management and promote climate-resilient farming practices