Development and Evaluation of Remote Sensing-Derived Relative Growth Rate (RGR) for Rice Yield Estimation

Crop yield estimation is crucial for improving agricultural management and ensuring food security. Accurate remote sensing-based yield estimation typically relies on the optimal selection of the most relevant variables as predictors from those variables, such as vegetation indexes and physiological indicators. However, although crop growth indexes from traditional crop growth analysis have been proven to correlate with crop yields, their potential for remote sensing-based yield estimation has not been explored. In this study, we developed a remote sensing-based formula to derive the Relative Growth Rate (RGR), a common crop growth index, and introduced it to the rice yield estimation models. Its estimation performance was evaluated by comparing with the Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Gross Primary Productivity (GPP), and Leaf Area Index (LAI). The results from 5-fold cross-validation showed that the RGR-based models outperformed the models based on other predictors both individually (R² = 0.71, RMSE = 832.50 kg/ha) and incorporated with environmental indicators (R² = 0.78, RMSE = 719.63 kg/ha). The RGR-based model exhibited superior spatial robustness and temporal scalability to other predictors and achieved an R² value above 0.75 up to 16 days before maturity for within-season estimation. Model interpretability analysis indicated that the RGR from the vegetative growth phase to the early reproductive phase was more influential than in the late reproductive phase, and yield increased as the growth rate rose in most periods. These findings suggest the potential of RGR in remote sensing-based yield estimation.