## 3 Results
### Comparison of Spring Phenology in Boreal Forests at the Site Scale
At the regional scale, SOSGOME-2 SIF exhibited slight differences compared to SOSIGSF and SOSEV2. This discrepancy may be attributed to the fact that GOME-2 SIF is built only based on primary satellite-retrieved SIF, while GOSIF incorporates discrete OCO-2 SIF soundings, vegetation greenness index EVI, and meteorological data [23]. Similarly, EVI2 also represents vegetation greenness [7; 38; 41; 42; 43]. Accordingly, in terms of spring phenology onset date, GOME-2 SIF showed slightly different results from GOSIF and EVI2, whereas no notable distinction existed between GOSIF and EVI2 due to the vegetation greenness information contained in both. Additionally, the inherent spatio-temporal resolution of the two satellites from which the SIF datasets were retrieved (daily, 50 km for GOME-2 and 16 days, 1.3 \(\times\) 2.3 km for OCO-2), the spectral wavelength used for SIF retrieval (740 nm for GOME-2 SIF and 757 nm for OCO-2 SIF), the spatial continuity (continuous for GOME-2 SIF and discontinuous for OCO-2 SIF), and even potential uncertainties in the calculations can lead to discrepancies in the final results. For instance, the accuracy of GOME-2 SIF may be limited by its lower spatial resolution, while the discontinuity of OCO-2 SIF may result in missing data in certain regions, thereby restricting the availability of photosynthesis phenology information there. In our study, we also assessed the reliability of remote sensing datasets by analyzing the spring phenology onset date across different coniferous vegetation types in boreal forests, as well as the corresponding temperature at the phenology onset date. Figure 4 illustrated that in mixed forests (MF), evergreen needleleaf forests (ENF), woody savannas (WS), and savannas (S), the phenology onset date derived from GOME-2 SIF significantly differed from those obtained from the other two remote sensing datasets. Specifically, SOSGOME-2 SIF indicated an earlier start date compared to SOSEV2 across all vegetation types, with a prominent exhibition in evergreen needleleaf forests, exceeding 10 days. This is supported by the research of [PERSON] et al. [7] and [PERSON] et al. [10], as well as the ecological principle that photosynthesis in evergreen needleleaf forests has begun before the emergence of new leaves [9]. From the perspective of temperature at SOS, [PERSON] et al. [7] reported that the daily maximum air temperature at the onset of photosynthesis in boreal evergreen needleleaf forests was 7.1 \(\pm\) 6.5 \({}^{\circ}\)C. [PERSON] et al. [44] noted that the needle leaf temperature could exceed the air temperature by 2-15 \({}^{\circ}\)C. [PERSON] et al. [45] also emphasized that leaf temperature, being higher than air temperature, was more closely related to spring phenology (e.g., leaf onset). By calculating both air temperature and land surface temperature (approximate to the canopy temperature) at SOS, we found that the temperature conditions had reached or exceeded 5 \({}^{\circ}\)C, no matter whether it was the daily maximum temperature or the daily maximum land surface temperature. These findings underscored the robustness of the spring phenology identified by GOME-2 SIF, GOSIF, and EVI2 across boreal forests. Our research highlighted the complementary roles of the three remote sensing datasets in studying spring phenology and its relationship with climate in boreal forests. At their optimal spatial resolution for phenology detection (3.5\({}^{\circ}\) for GOME-2 SIF, 0.05\({}^{\circ}\) for GOSIF, and 0.005\({}^{\circ}\) for EVI2), SOSGOME-2 SIF (_p_ < 0.01), SOSIGSF (_p_ < 0.001), and SOSEV2 (_p_ < 0.001) exhibited significant correlations with SOSFLUX-GPP, with correlation coefficients increasing alongside spatial accuracy (Figure 11). This suggested that spatial resolution of remote sensing datasets may have an influence on the comparison with ground-based observations. At the 3.5\({}^{\circ}\) grid scale, SOSs derived from GOME-2 SIF, GOSIF, and EVI2 showed significant correlations (\(p<0.05\)) with ground observations.