## 5 Conclusions  
This study delved into the capacity of SIF data, obtained from both ground-based and satellite measurements, to delineate crop growth stages, juxtaposed against EVI data, within the confines of our study site. It pioneered the harmonization of time-series phenological attributes with crop growth stages, thus underscoring the imperative need for aligning these two domains to effectively reconstruct their mapping relationship. The investigation unequivocally affirmed the effectiveness of SIF data within the phenological estimation framework for discerning the growth stages of both maize and winter wheat. SIF data emerged as the superior choice over EVI data, displaying marked advantages in terms of accuracy, robustness and sensitivity to phenological events. The remarkable precision achieved through the processing of SIF data, employing the best portfolio and 'bp' schemes, opens up promising avenues for further applications of SIF data in the domain of crop growth stage identification. Nevertheless, while this study offers valuable insights, it remains evident that an all-encompassing portfolio with fixed processing methods has yet to be fully unveiled. The pursuit of such a portfolio beckons further research endeavors, propelling the field closer to a unified and optimized approach for crop growth stage identification. Conceptualization, Y.Z. and Y.H. ; methodology, Y.H. ; software, Y.H. ; validation, Y.Z. and Y.H. ; formal analysis, Y.H. ; investigation, Y.H. ; resources, L.W., L.P., D.D., G.W., Z.L. and Y.W. ; data curation, L.W., L.P., D.D., G.W., Z.L. and Y.W. ; writing--original draft preparation, Y.H. ; writing--review and editing, Y.Z. and Y.H. ; visualization, Y.H. ; supervision, Y.Z., Z.L. and Z.Z. ; project administration, Y.Z. ; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (42125105, 42071388). The ungrafted TROPOMI SIF740 product from 2018 to 2022 adopted in this study was sourced from [[ftp://fluo.gps.caltech.edu/data/tropomi/ungridded/](ftp://fluo.gps.caltech.edu/data/tropomi/ungridded/)]([ftp://fluo.gps.caltech.edu/data/tropomi/ungridded/](ftp://fluo.gps.caltech.edu/data/tropomi/ungridded/)) (accessed on 8 September 2023) (for additional details, refer to: [[https://doi.org/10.22002/D1.1347](https://doi.org/10.22002/D1.1347)]([https://doi.org/10.22002/D1.1347](https://doi.org/10.22002/D1.1347)) (accessed on 8 September 2023)). The MODIS product utilized in this study is downloaded from the official website of United States Geological Survey at LP DAAC--MOD13C1 (usgs.gov) (accessed on 8 September 2023) and LP DAAC--MYD13C1 (usgs.gov) (accessed on 8 September 2023). We would thank [PERSON], [PERSON] and other researchers from Nanjing University for the installation of SIF and EC Flux equipment. Our appreciation also goes to [PERSON] and other researchers from ShangQiu station of Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences for their valuable contributions in the field measurements. The authors declare no conflict of interest. Figure 12: \(R^{2}\) for each maize growth stage estimations, from 2018 to 2022, using in situ SIF measured from “aftermoor” with varying estimation portfolios. For each growth stage estimation, the grey sector presents the lower \(R^{2}\) for the estimation, while the redder sector presents the higher \(R^{2}\). Figure 13: \(R^{2}\) for each maize growth stage estimations, from 2018 to 2022, using in situ SIF measured from ‘whole-day’ with varying estimation portfolios. For each growth stage estimation, the grey sector presents the lower \(R^{2}\) for the estimation, while the redder sector presents the higher \(R^{2}\). Figure 4: \(R^{2}\) for each maize growth stage estimations, from 2019 to 2022, using in situ EVI measured from ‘morning’ with varying estimation portfolios.