## 1 Introduction  
The importance of vegetation in the global carbon cycle is well known [1; 2; 3]. Understanding the response of vegetation growth to climate variations in recent decades is critical for projecting future ecosystem dynamics. Although relatively simple in vegetation structure, boreal forests and woodlands cover nearly 14.5% of the land surface and play an important role in the global carbon budget [4; 5; 6]. They are extremely sensitive to climate change and numerous studies have documented the enhanced terrestrial vegetation growth in the middle and high latitudes of the Northern Hemisphere over the past three decades [3; 7; 8]. The gross primary productivity (GPP) of a terrestrial ecosystem refers to the organic compounds that are accumulated by green plants on land through assimilating carbon dioxide in the atmosphere by photosynthesis and a series of physiological processes in plants [9; 10; 11]. Terrestrial GPP is the major driver of land carbon sequestration, and it plays a pivotal role in the global carbon balance, providing terrestrial ecosystems the capacity to partly offset anthropogenic CO\({}_{2}\) emissions [11; 12; 13; 14]. GPP varies diurnally and seasonally in response to both changes in climate (e.g., light, precipitation, temperature, and humidity) and nutrient availability, whereas the spatial distribution is determined primarily by climatic conditions [11]. To date, only a few studies [10] have reported on how the GPP of boreal forests vary with climate. The increasing availability of remote sensing measurements provides complete global coverage with a high revisit frequency for vegetation monitoring [15]. The popular data-driven approach to quantify vegetation variations at the global scales or regional scale is based on vegetation index, such as the NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index), which are directly related to vegetation activity [16]. However, the vegetation index can be affected by many factors that are unrelated to ecosystem structure or function, such as satellite drift, calibration uncertainties, inter-satellite sensor differences, and bidirectional and atmospheric effects [3; 17; 18; 19; 20; 21; 22]. Another method of vegetation monitoring is based on remotely sensed GPP models. The major approach to this method is to employ the maximum light use efficiency (LUE) [23; 24; 25; 26; 27; 28]. LUE is the core of physical models of global GPP products [29], including the Carnegie-Ames-Stanford Approach (CASA) [30; 31], Global Production Efficiency Model (GLOPEM) [28], Eddy Covariance-Light Use Efficiency (EC-LUE) [32; 33], C-Fix [34], MOD17 algorithm [35], Vegetation Photosynthesis Model (VPM) [6; 36], and C-Flux [37; 38]. Although the formulas and parameterization schemes vary among these models, the key idea is still the same, which is to combine all of those factors affecting GPP in a relatively simple linear or nonlinear way. The EC-LUE model is one of the most powerful GPP models and has been verified at 54 eddy covariance sites, suggesting that the EC-LUE model is robust and reliable [32; 33]. In addition, and the Global LAnd Surface Satellite (GLASS) GPP data using the EC-LUE model can provide a good data source for the study of GPP. Apart from modeling research, studies about spatiotemporal distribution and variation in GPP, as well as its influence mechanism, are also hot topics [11; 39; 40]. GPP can be affected by many factors, such as fire, logging, harvesting, insect outbreaks, and ecosystem dynamics [11]. Temperature was reported as the main factor of GPP variability in the middle and high latitudes of the Northern Hemisphere [7; 41; 42]. Phenological parameters can also result in large changes in annual GPP [43]. The forest ecosystems in northeast China play an important role in the national carbon budget because they comprise more than 30% of the total forest area in China [44].