## 1 Introduction  
Vegetation phenology refers to the stages of plant development, such as germination, leaf growth, flowering, fruiting, and defoliation, and their regular changes in response to climate variations [1, 2, 3, 4]. While both forest and crop phenology follow seasonal cycles, there are fundamental differences between them, primarily in temporal scale and ecological function [5]. Crop phenology operates on a shorter annual cycle, directly managed by human agricultural activities, whereas forest phenology unfolds over much longer timescales and is influenced primarily by natural environmental factors [6, 7]. Unlike crops, which are often harvested within a single season, forest ecosystems exhibit continuous carbon accumulation over decades, making phenology a critical factor in long-term carbon balance assessments [8, 9, 10, 11]. Research has underscored the critical role of phenology in monitoring ecosystem responses to climate dynamics. For instance, [PERSON] found that rising temperatures lead to earlier leaf unfolding and delayed leaf senescence, thereby lengthening the growing season [11]. Similarly, [PERSON] observed that accelerated spring warming in temperate regions of the Northern Hemisphere triggers earlier phenological events [8]. These shifts have significant consequences for ecosystem water, heat, and carbon cycles [12]. Therefore, it is important to understand the impacts of phenological changes and climate change on carbon cycling in forest ecosystems [13; 14]. Vegetation phenology is a key factor influencing the productivity of forest ecosystems, and its variations can provide valuable insights into the carbon balance of terrestrial ecosystems [15; 16]. The length of the growing season in forests, defined by the period between leaf-out in spring and senescence in autumn, is a crucial determinant of forest productivity and carbon sequestration potential [15; 17; 18]. Gross primary production (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) are key indicators of the carbon cycle, measuring the total carbon captured by plants through photosynthesis [19; 20; 21]. [PERSON] [22] suggested that warming promotes an early spring and late fall, increasing the LOS and thus, carbon storage in temperate forests. [PERSON] [23] examined the effect of phenology on C flux between 1980 and 2002 in the Northern Hemisphere, concluding that LOS was closely tied to GPP, with a longer LOS ultimately promoting vegetation growth. Deforestation is a critical factor influencing the forest carbon cycle, as it leads to significant carbon emissions by reducing biomass and altering soil carbon dynamics [24; 25]. Large-scale deforestation has been linked to increased atmospheric CO\({}_{2}\) concentrations and reduced global carbon sequestration capacity [26]. Remote sensing has the characteristics of high resolution and wide coverage, which is helpful to accurately assess forest cover change and carbon cycle monitoring [27; 28]. The forest ecosystem carbon cycle is a key component of the global terrestrial carbon cycle, and carbon cycle simulations have become essential for studying this process [29]. Forest carbon cycle models are mainly divided into statistical models, remote sensing parameter models, and process-based models. Statistical models use observed data to reveal patterns in carbon cycling through statistical analysis. Remote sensing parameter models are based on remote sensing data, while process-based models optimize parameters by integrating climatic conditions, soil, vegetation physiological, and geographic location. Notable examples of process-based models include the TEM, CENTURY, BIOME-BGC, BEPS, and InTEC models. Among these, the InTEC model is particularly effective as it simulates both the carbon and water cycles by incorporating a variety of environmental variables. The integration of remote sensing technology with process-based models, such as InTEC, has proven to be a powerful tool for simulating and monitoring the forest carbon cycle.