## 2 Materials and Methods
### Statistical Analyses  
The spring phenology derived from GOME-2 SIF was calculated at the primary spatial scale of 0.5\({}^{\circ}\), which indicated a notably low correlation with the ground FLUX-GPP phenology, leading to significant uncertainties. To address this, experiments were conducted to determine the optimal spatial scale for calculating spring phenology based on GOME-2 SIF. Firstly, the GOME-2 SIF raster data were resampled from 0.5\({}^{\circ}\) to 5\({}^{\circ}\) in a step of 0.5\({}^{\circ}\), with data aggregated at each scale by averaging the original pixels [40]. Subsequently, the spring phenology was calculated for grids containing flux sites at each scale. At last, regression analysis was performed between the phenology of these grids and the corresponding flux sites. The optimal spatial scale was determined as the resolution where the correlation coefficient passed an extremely significant test (\(p<0.01\)) and remained stable at coarser scales (i.e., the correlation coefficients remained significant). In order to facilitate comparative analysis, all three remote sensing datasets were aggregated to this optimal spatial scale. At the same time, pixels containing PEP725 and flux sites were selected for subsequent analysis.  
To assess the difference and consistency between the spring phenology estimated from remote sensing datasets and the ground observations, we employed difference analysis, Kruskal-Wallis non-parametric tests, and multiple linear regression analysis for comparison.  
In comparison of spring phenology estimated from diverse datasets in boreal forests, we conducted the following analyses:  
1. Comparison and verification of phenology onset date: We validated spring phenology derived from multiple remote sensing datasets against ground observations at two spatial scales: a uniform 3.5\({}^{\circ}\) scale and their respective optimal spatial scales for phenology detection (3.5\({}^{\circ}\) for GOME-2 SIF, 0.05\({}^{\circ}\) for GOSIF, and 0.005\({}^{\circ}\) for EVI2). Additionally, we compared SOS of different coniferous vegetation types and the corresponding temperature at SOS.
2. Exploration of SOS-climate relationships at the site scale: We investigated the relationship between land surface phenology and climate factors using partial correlation coefficients (\"Pcor\") as the primary metric. Key climate variables included daily maximum temperature, daily minimum temperature, precipitation, and solar radiation. Specifically, we analyzed preseason climate variables (mean temperature, total precipitation, and total radiation) over varying time spans (30, 60, and 90 days) preceding the multi-year mean SOS at each site and its corresponding grid. For FLUX-GPP data sites, we applied partial correlation analysis to assess GPP-based phenology (\(\text{SOS}_{\text{FLUX-GPP}}\)) and FLUX climate variables. For PEP725 phenological observation sites, we conducted partial correlation analysis between \(\text{SOS}_{\text{PEP725}}\) and ERA5 climate factors at a 0.1\({}^{\circ}\) spatial resolution. Similarly, for GOME-2 SIF, GOSIF, and EVI2, we examined SOS-climate relationships between the phenology of the grids where the sites are located and ERA5 climate factors at a uniform spatial scale.