## 4 Discussion
### Evaluation of Elements in The Estimation Portfolio  
In the assessment of elements within the crop growth stage estimation portfolio, this study delves into the combination of factors, including data-measured time periods, compositing methods, phenological curve-fitting models and transition characterization methods. While previous research has acknowledged these elements as potential solutions for specific situations, there remains a dearth of comprehensive comparisons among them [66; 68]. Commencing with the selection of data-measured time periods, the findings reveal divergent results for maize and winter wheat growth stage estimation using SIF data (Figure 6). 'Afternoon' emerges as the optimal measurement time for winter wheat, while 'Morning' proves superior for maize. This discrepancy can be attributed to the midday depression in photosynthesis that the intensity of photosynthesis have been found reduced during the heat wave at mid of the day [80]. At the same time, this depression in photosynthesis was tracked by SIF observation for its sensitivity with the gross primary production loss [21; 46]. This sensitivity of SIF underscores the influence of the midday depressionon constructing phenological time-series curves. The difference between winter wheat and maize is linked to variations in midday temperatures during their respective growing seasons in Shangqiu. Additionally, 'Afternoon' emerges as the preferred measurement time for EVI, indicating EVI's insensitivity to midday photosynthetic depression. The impact of compositing methods on phenological monitoring has been discussed in prior studies, with appropriate methods shown to reduce systematic errors in estimations by mitigating sensitivities to factors such as canopy structure, chlorophyll content and biomass [71; 81; 82]. Compositing methods encompass composited values and lengths of composited intervals. In terms of the length of composited intervals, this study suggests that for datasets utilized in maize KGS estimation, an interval of approximately 7 days is most suitable (Figure 7). Conversely, for datasets employed in winter wheat KGS estimation, the ideal composited interval ranges from 1 day to 3 days. However, regarding composited values, it remains challenging to draw a definitive conclusion, with a vague trend indicating that MVC and AVC are preferable for EVI and SIF datasets, respectively (Figure 8). The findings concerning the impact of compositing methods on crop growth stage estimation lack the robustness required for general applicability. Furthermore, this study explores the combinations of phenological curve-fitting models and transition characterization methods as potential sources of diversity in phenological event detection [6; 42; 68]. Yet, it is essential to streamline the phenological estimation framework, avoiding redundancy. As shown in Figure 9, the study attempts to identify an acceptable combination of models and methods to simplify the process, with Gu-based models demonstrating superior performance in most cases, if supplied with enough data [73]. However, the advantages of different phenological transition characterization methods remain unclear, as accuracy assessment results are distributed randomly. As a result, a robust combination suitable for general scenarios could not be ascertained in this case study. The combination of land surface model and agronomy crop model has been proved capable for improving maize growth processes simulation [83]. Remote sensing observations covering heat, water and photosynthesis assimilated into crop models could also improve crop phenological transition characterization. Thus, future research conducted on a larger scale with more extensive datasets assimilated into crop models may shed light on this issue. In summary, this study scrutinizes individual components of the crop growth stage estimation framework, aiming to identify a unified portfolio for simplification.