Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API  improved the accuracy of yield prediction than single-phase UAV imagery-based feature  [single-phase model VIs&Tex + API: the coefficient of determination (R²) between 0.502–0.732, the root mean square error (RMSE) between 0.295–0.499, the relative root mean square error (RRMSE) between 0.073–0.122] with the panicle initiation stage being the best period for yield prediction (R² as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi-temporal method can increase R² by 0.020–0.111 and decrease RMSE by 0.020–0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.

ratoon rice; yield prediction; unmanned aerial vehicle; multi-temporal feature