Jing Zhou

Alfalfa is one of the most widely cultivated perennial legume crops used as feedstock for animals. Efficiently estimating alfalfa yield and quality traits before harvesting is critical for the decision-making process regarding precision management activities and harvesting time to ensure high profitability. Satellite-based radar is a powerful tool in remote sensing for crop monitoring because it provides high-quality data regardless of weather conditions. Therefore, this study aims to investigate the potential use of satellite radar features and environmental factors in estimating alfalfa yield and quality. Alfalfa yield and quality traits, including dry matter yield (DMY), crude protein (CP), neutral detergent fiber (NDF), NDF digestibility (NDFD), and acid detergent fiber (ADF), were collected over 16 alfalfa fields from 2016 to 2021, leading to 126 samples in total. Sentinel-1 radar backscattering coefficients and environmental factors were collected for all the fields across all growing seasons. Five commonly used machine learning models were established to estimate each alfalfa trait separately. The results show that the Extreme Gradient Boosting model consistently performed the best for all alfalfa traits. The accuracy of the DMY estimates is acceptable, with an average R2 of 0.67 and an RMSE of 0.68 tons/ha. The best result for estimating CP was an average R2 of 0.70 and an RMSE of 1.63% DM. In estimating alfalfa fiber indicators (i.e., ADF, NDF, and NDFD), we achieved the highest average R2 values of 0.54, 0.62, and 0.56, respectively. Overall, this study demonstrated the potential use of environmental factors for alfalfa yield and quality estimation in-field before harvesting. However, the Sentinel-1 radar backscattering coefficients did not make significant contributions to improving the estimation performance, compared to the environmental factors.