## 4 Discussion
### Harvest Components Models from Remote Sensing  
The significant differences in yield components and quality parameters between fertilizing treatments reflect the expected effect of nutrient availability on crop growth and development. The high yields observed in plots treated with P(MAP) + N(Urea) and P(STR + C) + N(Urea) highlight the effectiveness of these fertilization strategies. The increase in protein content and hectoliter weight in response to N fertilization is consistent with known physiological responses in wheat, where late season N availability supports protein synthesis and improves grain quality. Interestingly, the unfertilized control had the highest starch content, suggesting that limited nitrogen availability may promote starch accumulation in grain, a finding consistent with previous studies on carbohydrate partitioning under nutrient-limited conditions [83; 84].  
The moderate performance of the PlanetScope model (R\({}^{2}\) from 0.49 to 0.56) suggests that while it can capture general trends in crop growth and yield, it may struggle to detect the finer-scale variability induced by different fertilization strategies due to its coarser spatial resolution. This limitation may affect its sensitivity to small-scale variations in crop cover and biomass often present in field trials. However, the model's ability to predict starch content (R\({}^{2}\) = 0.56) and hectoliter weight (R\({}^{2}\) = 0.51) demonstrates its potential for monitoring broad trends in crop quality, particularly over large-scale farms. These results highlight PlanetScope's value in leveraging high temporal resolution for identifying critical phenological stages, especially during rapid growth phases after fertilizer application, where predictions were strongest. For instance, starch content predictions effectively captured accumulation patterns driven by nitrogen application, while hectoliter weight predictions reflected the role of early-season phosphorus and nitrogen fertilization in maintaining grain density. These findings underscore the utility of PlanetScope in addressing the study's hypothesis, particularly, in enhancing precision and sustainability in crop management. In contrast, the poor performance of the MicaSense model (R\({}^{2}\) < 0.30) suggests that its spatial resolution and spectral sensitivity were insufficient to detect meaningful relationships between the experimental treatments and crop outcomes. The MicaSense sensor appeared to discriminate between the control and fertilized plots but was unable to detect the more subtle differences between different fertilization strategies. This limitation could be due to a combination of factors, including the sensor's reduced ability to capture variability, the problems of calibration, and its sensitivity to environmental conditions, that could interfere with accurate fCover retrievals.  
Overall, while PlanetScope provided moderate predictions of yield components and crop quality, the MicaSense model was not effective in this context. The higher spatial resolution of MicaSense, which may be an advantage in other scenarios, did not translate into better prediction accuracy for yield components in this experiment. Nevertheless, in this experiment, the PlanetScope satellite constellation provides useful information for assessing the phenological development of winter wheat and yield retrieval at the plot scale.