## 3 Results
### Comparison between Time-Series Phenological Estimation Portfolios for Crop Growth Stages
#### 3.2.3 Compositing Methods  
The concept of compositing methods pertains to the manner in which data are combined--whether by taking the maximum or average value--and the duration of time intervals between each pair of compositing dates during the observation period. This section aims to assess how different compositing methods influence estimation results and identify, where possible, the most effective compositing method. Notably, the MODIS EVI product used in this study was already composited into 16-day intervals using the MVC method. Consequently, satellite EVI data are not considered in this section. Furthermore, in situ EVI data were excluded for winter wheat due to its documented performance issues, as previously discussed. This section begins by investigating the impact of different time interval lengths between compositing dates on estimation accuracy. In Figure 7, it presents the distribution of estimations for maize and winter wheat KGSs using SIF datasets with varying composited intervals, categorized accordingly. Each observation (OB) point, represented by a red dot, on the chart illustrates the distribution of observed KGSs for each target crop, and this pattern applies to the other points as well. The distance between each point and the origin indicates the precision (standard deviation) of estimations using the corresponding time interval, while the distance between each point and the observation point signifies the accuracy (the root-mean-square-error, RMSE), represented by the point's color. Additionally, the 'Correlation' value on the arc also reflects the accuracy (R) of each estimation. As discussed in Section 3.2.1, EVI datasets show limited capability in estimating all KGSs accurately when treated as a complete dataset, although they may perform well for specific KGSs (e.g., T&S of maize, as seen in Figure 6). Consequently, EVI datasets have been excluded from this analysis. In general, the standard deviations (STDs) of most ground-based observations are approximately 25 (except for the area observation of winter wheat), which corresponds to the average lengths of the key growth periods for both crops. Likewise, the STDs of most estimations fall within the same scale, except for maize with TROPOMI SIF, where the STD is approximately 20. The RMSEs for most estimations are lower than 20, and RMSEs for estimations of winter wheat and maize with in situ SIF are even below 10. This suggests that most SIF datasets are capable of providing estimations with good accuracy and precision, with estimations for winter wheat generally exhibiting slightly higher accuracy than those for maize. Comparing estimations with TROPOMI SIF, most of which provide correlations (R) higher than 0.7, indicating a decent level of accuracy. Estimations with in situ SIF consistently offer correlations approximate 0.9 or even higher,highlighting their remarkable accuracy. Regarding the ideal compositing intervals, it is important to choose not only the ones with lowest RMSEs, but also considering the STDs and correlations. It is advisable to choose 15 days and 7 days when estimating KGSs for maize and winter wheat, respectively, using in situ SIF. For TROPOMI SIF, the optimal compositing intervals are 7 days for maize KGSs, while no compositing (1d) is recommended for winter wheat KGSs. Notably, the divergence in ideal compositing intervals for the two crops across varying compositing methods underscores the challenge of finding a single compositing method suitable for accurately estimating KGSs for each dataset. The choice of composited value is another crucial aspect of the compositing method. In the case of maize growth stage estimation using in situ SIF, as depicted in Figure 8a, it is evident that MVC outperforms AVC, as indicated by consistently higher \(R^{2}\) for each  
Figure 7: Taylor diagrams for estimations of key growth stages (KGSs) estimations using varying lengths of composited intervals.