Scaling Biomass Estimation by Expanding Ground Truth with UAS-Derived Training Data

Accurate estimation of winter cover crop biomass at a landscape scale is key to assessing benefits and promoting widespread adoption. Satellite imagery offers broad coverage but is limited by coarse resolution and spatial mismatch with field measurements. This study introduces a hybrid framework to improve satellite-based estimation of cereal rye cover crop biomass by integrating UAS-derived data. Extreme gradient boosting (XGBoost) and random forest (RF) machine learning models were trained across three scenarios: (1) UAS-based models, using field-measured biomass alongside UAS-derived vegetation indices (VIs) and crop height; (2) satellite-based models, using field-measured biomass and Sentinel-2 satellite-derived VIs and grey level co-occurrence texture measures; and (3) UAS–satellite synergistic models, where UAS-estimated biomass served as surrogate ground truth for calibrating satellite-derived VIs and texture features. Our results show that the error increased by up to 49% for XGBoost and 31% for RF when using field-measured cereal rye biomass at a 0.5 × 0.5 m2 to directly train satellite-derived features with 10 m resolution (RMSE = 83.09 g m−2 for XGBoost and 80.46 g m−2 for RF), compared to using UAS-derived features at 5 cm (RMSE = 55.78 g m−2 for XGBoost and 61.63 g m−2 for RF). Notably, the UAS–satellite synergistic model demonstrated improved alignment with RMSE of 59.79 g m−2 for XGBoost and 61.45 g m−2 for RF while potentially overcoming the limitations due to differences in the size of satellite pixels and field measurements. These findings underscore the potential of UAS-derived biomass estimates to improve the accuracy, scalability, and spatial fidelity of satellite-based cover crop biomass estimation.