## 1 Introduction  
Gross Primary Productivity (GPP), defined as the total amount of carbon fixed by vegetation through photosynthesis, is one of the most important components of the terrestrial carbon cycle [1; 2]. Meanwhile, it is also the largest carbon flux between the terrestrial biosphere and the atmosphere, and drives a variety of ecosystem services such as food, fiber, and wood production [3]. The Tibetan Plateau (TP), with an average elevation exceeding 4000 m, is one of the most sensitive regions to global climate change [4]. Moreover, due to the positive feedbacks associated with the melting of snow and ice, the warming rate on the TP is expected to be much higher than other regions over China [5; 6]. Such a unique geographical environment makes the circulation of carbon on the TP different from other regions. Therefore, accurate estimation of GPP on the TP and continuous monitoring of its spatial and temporal dynamics are needed to enhance our understanding of terrestrial ecosystems and global carbon cycling. As of now, many models have been developed to estimate GPP at the regional scale, which can be divided into statistical models [7], light use efficiency models [8; 9], and process-based models [10; 11; 12]. Based on these models, numerous studies have quantified GPP of alpine grasslands on the TP [13; 14; 15; 16]. However, it should be mentioned that there are large differences between the GPP values estimated by different studies, which greatly impeded our understanding of the feedback mechanisms between the terrestrial ecosystems and the atmosphere. For example, [PERSON] et al. [13] estimated annual GPP of alpine grasslands on the TP as 712 gC/m\({}^{2}\)/yr by the Terrestrial Ecosystem Model (TEM), which is 2.28 times the estimate of [PERSON] et al. [14], in which annual GPP of alpine grasslands was quantified at 312.3 gC/m\({}^{2}\)/yr by the Vegetation Photosynthesis Model (VPM). The large model uncertainty may be attributed to the inadequate calibration of model parameters, especially for complex process-based models. Therefore, it is necessary to develop an effective parameter calibration method capable of adapting the model to particular applications. The Biome-BGC model is a typical process-based biogeochemical model that can be used to simulate the carbon, nitrogen, and water fluxes within terrestrial ecosystems [11]. Initially, the application of the Biome-BGC model was mainly focused on forest ecosystems [17; 18; 19]. Recently, some studies have applied this model to grassland ecosystems [20; 21]. However, in addition to the aforementioned difficulties of parameter calibration in the process-based model, the phenology module of the Biome-BGC also has limitations, especially when applied to alpine regions. Specifically, this phenology module assumes that leaf onset occurs when both the summed soil temperature and the summed precipitation are greater than the critical values, and leaf senescence occurs when the date has passed July 1 st and the average minimum temperature for the next 3 days is less than the annual average minimum temperature [22]. Nevertheless, for alpine regions like the TP, frozen soil, glaciers, and snow are usually widely distributed, and their seasonal thawing can provide water for the germination and growth of alpine vegetation, which in turn will affect phenological shifts [23]. Unfortunately, the original phenology module in the Biome-BGC has not taken into account the effects of frozen soil and snow, which caused a significant delay in the simulated leaf onset date and further led to a severe underestimation of GPP [21]. Therefore, it is necessary to modify the phenology module of the Biome-BGC to make it applicable to alpine regions. In recent years, the development of remote sensing technology has made it possible to extract phenological parameters on the regional scale.