Satellite-based monitoring of forage quality in grasslands of the United Kingdom using sentinel-2 data and random forest regression

Introduction: In the temperate grasslands of the UK, forage quality is a key factor influencing both animal performance and environmental impact. Because forage quality strongly affects rumen fermentation, improving it can reduce enteric methane emissions and mitigate animal nutritional stress. However, large-scale monitoring of forage quality remains limited due to the reliance on destructive, labor-intensive, and costly sampling methods. Remote sensing offers a promising alternative for scalable monitoring. Methods: We explored an indicative approach combining optical remote sensing (Sentinel-2) with random forest regression (RFR) models to predict four critical forage quality attributes: crude protein (CP), water-soluble carbohydrates (WSC), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Calibration and validation were performed using >9,500 georeferenced observations collected between 2020 and 2022 at the North Wyke Farm Platform in southwest UK. Forage quality was measured using near-infrared (NIR) sensors mounted on agricultural machinery across paddocks containing permanent and improved pastures. Sentinel-2 spectral predictors included visible, NIR, and red-edge bands, and model performance was evaluated using R² and RMSE metrics. Results: Model performance was strong across all four forage quality attributes, with R² values ranging from 0.77 to 0.86 and consistently low RMSE values, indicating high predictive accuracy. Red-edge and NIR wavelengths were the most influential predictors. Improved pastures generally exhibited higher forage quality—characterized by lower ADF and higher WSC concentrations—than permanent pastures. Model-predicted seasonal changes were modest, whereas spatial contrasts between paddocks were much more pronounced. Discussion: The calibrated models are suitable for forage systems with species composition and quality ranges similar to those represented in our dataset but should not be directly applied to other forage types without recalibration. Overall, this work demonstrates the potential of Sentinel-2 remote sensing combined with machine-learning approaches for tolerably accurate, largescale forage quality monitoring. Such advancements could help improve grazing management, support nutritional planning, and contribute to efforts aimed at reducing methane emissions from livestock systems.