## 4 Discussion
### fCover Retrieval Using Remote Sensing Dataset  
The results show that while the PlanetScope-based models are useful for capturing general trends in fractional vegetation cover (fCover), they are limited in their ability to discriminate finer-scale effects of different agronomic treatments. The relatively low standard deviations and moderate correlations suggest that the coarser spatial resolution of PlanetScope likely limits its ability to detect the subtle heterogeneity present within experimental plots. This limitation is particularly evident in treatments with varying levels of phosphorus and nitrogen fertilization, where spatial variability in plant cover is expected to be higher due to differential nutrient uptake. Among the PlanetScope indices, the Enhanced Vegetation Index (EVI) was the most effective predictor, yet it still exhibited limited sensitivity and may have underestimated the influence of certain treatments on crop cover. Although the satellite-derived fCover models capture general seasonal trends, they have limitations in distinguishing between specific fertilization products and combinations. This is evident during the post-peak period, where fCover is often overestimated, particularly in treatments involving compost and nitrogen-based fertilizers. For example, treatments such as P(BM + C) + N(Urea) show higher modeled fCover than expected toward the later stages of the season. Despite these discrepancies, the fCover models effectively reflect overall temporal patterns of crop growth and provide useful insights into the relative effects of different fertilization strategies on vegetation development at the plot scale. However, the tendency to overestimate fCover in fertilized plots, especially those receiving higher nutrient doses (e.g., compost combined with urea), underscores one of the key limitations of the models. In contrast, the superior performance of the RedEdge-M model, particularly in the Normalized Difference Vegetation Index (NDVI) model (R\({}^{2}\) = 0.82), can be attributed to the higher spatial resolution of the sensor, which allows for more accurate detection of fCover variability across experimental plots. This increased variability, reflected in the higher standard deviations, suggests that RedEdge-M can better capture the effects of different fertilizer treatments on crop cover, especially in the more heterogeneous parts of the field. For example, treatments involving compost and organic nutrient applications, which typically cause localized variations in soil fertility, are better detected by the RedEdge-M sensor. This increased sensitivity to within-field variability is critical for accurately monitoring crop response to nutrient treatments and improving precision farming strategies. It is particularly noteworthy that NDVI, an index traditionally recognized for its ability to minimize directional effects, outperforms other indices from the RedEdge-M sensor. By utilizing the red and near-infrared (NIR) bands, NDVI may be less influenced by calibration problems, which could explain its superior performance in this study. This observation is especially intriguing given the earlier challenges encountered with MicaSense's calibration inconsistencies, as discussed in prior sections. Despite the sensor's known issues, NDVI's robustness appears to mitigate some of the calibration-related drawbacks, allowing for a more reliable detection of fCover variability in experimental plots. Given the variability observed between the datasets, data fusion presents a promising avenue to enhance the synergistic analysis of vegetation dynamics. However, before implementing such approaches, it is essential to address the quality of surface reflectance measurements to improve intercomparability. Ensuring consistency in reflectance data through ground-based validation and addressing differences in radiometric calibration or acquisition protocols will provide a robust foundation for effective integration.