## 4 Discussion  
### Phenology Metrics and Thermal Evolution  
The use of PlanetScope imagery in this study has proved effective in monitoring the phenological development of winter wheat. The alignment of NDVI values with key phenological stages, as outlined by the Zadoks scale, suggests that satellite-derived data can provide accurate insights into the temporal dynamics of crop growth. The detection errors associated with SOS and EOS (8 days and 12 days, respectively) are acceptable given the scale of remote sensing data and its inherent temporal resolution. Also, note that the field-level SOS represents the souring date, while the SOS derived from remote sensing corresponds to the crop's emergence. As expected, this remote sensing-based SOS is detected later than the souring date. Similarly, for EOS, the satellite detected the end of the season sooner than the actual date observed in the field, likely due to differences in how crop senescence is captured from satellite data compared to ground-based observations. These results highlight the ability of PlanetScope's high temporal resolution to effectively track vegetation dynamics, allowing accurate detection of phenometrics such as SOS and EOS. PlanetScope's frequent revisits provide a significant advantage in capturing rapid temporal changes in crop development, ensuring that key phenological stages are accurately monitored throughout the growing season. Previous studies have emphasized the importance of temporal resolution for accurately capturing land surface phenology metrics, further supporting the effectiveness of PlanetScope in addressing these challenges [73]. Furthermore, the steady increase in NDVI during tillering and the sharp increase leading up to grain development demonstrate the sensitivity of NDVI to changes in biomass and canopy cover. The correlation between NDVI and GDD further supports the use of remote sensing data to predict key phenological events, which could help to optimize agricultural management practices, such as fertilizer application and irrigation scheduling. The results show a clear correlation between the satellite-derived NDVI values, the phenological stages described by the Zadoks scale, and the accumulation of GDD. The NDVI patterns and their relationship with GDD provide a robust framework for understanding winter wheat development throughout the growing season. The combination of thermal time models, such as GDD, and observations based on the Zadoks scale is particularly valuable in capturing the full complexity of crop development. While GDD provides a continuous, temperature-driven metric that can be applied to different agroecosystem conditions [74], the Zadoks scale provides detailed insights into specific phenological stages, which are critical for local field management. As highlighted by [PERSON] et al. (2023) [75], GDD and other thermal indices are more easily scalable over larger regions, making them ideal for remote sensing applications, while the Zadoks scale remains critical for precise, site-specific observations. The integration of both approaches improves the accuracy of phenological monitoring and ensures a comprehensive understanding of crop growth dynamics under varying environmental conditions. The observed changes in NDVI across the phenological stages provide valuable insight into the relationship between biomass accumulation and thermal time. During the early stages of development, the NDVI effectively captures the initial vegetative growth, reflecting the sensitivity of remote sensing data to small increases in canopy cover. As the crop transitions into the tillering and grain development stages, the strong correlation between NDVI and GDD highlights the ability of thermal time models to predict key phenological events and canopy dynamics. Similar GDD ranges have been reported in other studies investigating winter wheat development, confirming the scalability of GDD as a reliable metric for monitoring crop growth [76].