## 1 Introduction  
Vegetation phenology is a key part of the carbon flux in terrestrial ecosystems [1]. Understanding the time of phenology onset and cessation, as well as its relationship with climate factors, is vital in the study of global changes, such as carbon and water cycles, particularly in the context of climate change [2, 3, 4, 5, 6]. There have been numerous studies on spring phenology using either remote sensing data or ground observations [7, 8, 9]. However, the timing of the spring phenology obviously fluctuates across different datasets and may yield diverse conclusions in some cases. [PERSON] et al. [7] reported that spring phenology (SOS for 16-day GOME-2 SIF) was DOY 103 \(\pm\) 31 for evergreen needleaf forests, while [PERSON] et al. [10] found it (SOS for monthly GOME-2 SIF) to be DOY 125 \(\pm\) 6. [PERSON] et al. [11; 12] highlighted a 70-day difference in spring phenology based on the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in some areas of the United States and revealed variations ranging from several days to several dozen days in spring phenology (green-up date) in six remote sensing datasets. Therefore, when utilizing the current remote sensing datasets for studies related to spring phenology, it is essential to evaluate and validate them as comprehensively as possible. In ground observations, FLUX-GPP, measured by the eddy covariance method [13], represents carbon absorption through plant photosynthesis and has been used to validate the physiological dynamics of vegetation from remote sensing datasets, such as solar-induced fluorescence (SIF), in recent years [9; 10; 14]. Another ground observation dataset, PEP725 (the Pan European Phenology network), is a database encompassing more than 200 plant species from the northern temperate zone, from 1951 until now, with over 13 million records covering 70 growth stages. It is mainly used for research on leaf onset and can theoretically validate the \"greenness\" change in the vegetation index products [15; 16; 17]. In satellite observations, the MODIS phenological product MCD12Q2, derived from EVI2, is effectively applied to identify the spring phenology of boreal forests, but it is limited to reflecting the dynamics of vegetation ecosystems, with subtle visual seasonal dynamics [7; 9]. Moreover, SIF can effectively simulate the dynamic photosynthetic process of vegetation, particularly in evergreen needleaf forests, and it is becoming more and more popular due to its effects on reducing disturbances from background factors, such as clouds, soil, shadows, snow, and water [3; 18; 19; 20]. At present, only a few spring phenology studies have been conducted using primary satellite-retrieved SIF [7; 21; 22], and almost all SIF datasets for land surface phenology (LSP) estimation are high-resolution products developed based on SIF soundings, the vegetation index, meteorological data, etc. (e.g., GOSIF [23] and CSIF [24]). These products have been used in regional- and global-scale research relevant to land surface phenology [8; 25], yet the difference and consistency between them and primary SIF data in the monitoring of boreal forest phenology remain unclear. The harsh environment, fragile ecology, and sparse ground observation sites of boreal forests make large-scale phenology research in this region rely on remote sensing data [7; 26; 27]. As the most widely distributed coniferous forest zone in the world, boreal forests have a key role in the carbon cycle and provide an opportunity for interpreting the phenology dynamics using primary satellite SIF, despite its relatively low spatial resolution [16; 28; 29]. Based on the above understanding, we aim to find the difference and the consistency of spring phenology onset dates in boreal forests by using diverse datasets. We hypothesize that the spring phenology onset, estimated from the primary SIF, may exhibit some differences when compared to the results obtained from the modeled SIF and the vegetation index, but it remains functional.