## 3 Results and Discussion
However, the temporal component of phenological modeling, quantified as DOYs of occurrence of the evaluated phenological metrics, produced the most influential covariates, with DOY (EOS) and DOY (SOS) being crucial for the yield potential prediction in particular years for maize and soybean, respectively. These results clearly suggest that vegetation indices extracted from specific time windows were consistently more predictive than those from entire season aggregates. This is in agreement with [PERSON] et al. [29], who identified 80-105 days after planting as the optimal period for yield prediction in sunflower. However, RF, as the most accurate machine learning method of those evaluated in this study, generally distributed importance across multiple phenological stages, highlighting its capacity to utilize complex, non-linear feature interactions. With exceptions for DOY (EOS) in maize yield potential prediction, covariates based on EOS and dormancy remained consistently low in importance, which likely reflects the limited relevance of late-season vegetation activity to the final yield, especially given that Sentinel-2-derived reflectance in these periods may be impacted by senesced biomass or post-harvest artifacts [81]. Considering the importance of vegetation indices used for the calculation of aforementioned phenological metrics, NDVI consistently resulted as the most important predictor for maize yield potential prediction across all years, particularly in 2020 and 2022, with WDRVI also resulting in moderately high importance in the same period (Figure 5). However, NDVI3 RE was a dominant vegetation index for soybean yield potential prediction, which is characterized by an increased resistance to saturation effect due to the increased leaf area index [53]. While EVI and EVI2 produced a moderately low, but consistent, importance for both maize and soybean yield potential prediction, IDVI and PPI produced a very low overall importance, suggesting that they are not suitable for yield potential prediction. These observations are aligned with the results from a previous study based on a correlation analysis between used vegetation indices and crop yield data, which were obtained from a yield mapping system of combine harvesters [26], suggesting that their interaction with crop growth dynamics and soil ground were the main drivers of the achieved prediction accuracy. Similar relative variable importance metrics among EVI and EVI2 vegetation indices and SOS and greenup phenological metrics suggest the potential presence of multicollinearity, as correlated predictors may obscure the contribution of individual variables. However, RF was generally proven to be resistant to multicollinearity [64], which benefits the interpretability of variable importance values in the entire vegetative period of maize and soybean. While this study provided a comprehensive evaluation of the effectiveness of phenological metrics in crop yield potential prediction, the main limitation of this study is related to the reliability of ground truth crop yield data. Although access to crop yield data has expanded significantly in recent years, comprehensive ground truth yield observations remain scarce, especially at large spatial scales [32]. This research addressed this gap by integrating a QDANN dataset at a spatial resolution of 30 m that was based on extensive yield mapping data but included statistical modeling and, thus, contains a bias in its data with a RMSE of 2.29 t/ha for maize and 0.85 t/ha for soybean when validated against field-level measurements [46]. Furthermore, because the models were trained on modeled rather than directly observed yield, an underestimation or overestimation of prediction accuracy might have occurred, depending on the alignment between QDANN outputs and actual field-level yields. Since the QDANN dataset represents an estimate rather than a direct field measurement, any systematic bias or random error in the modeled yield values was incorporated into the training and validation datasets.