## 1 Introduction  
Terrestrial ecosystems have absorbed 25-30% of anthropogenic carbon dioxide emissions in the past five decades [1]. Vegetation is an essential part of terrestrial ecosystems and plays a crucial role in regulating regional climates and maintaining the surface energy balance [2, 3]. Gross primary productivity (GPP) refers to the total amount of organic carbon fixed by plants in the ecosystem by absorbing solar energy and assimilating carbon dioxide through photosynthesis per unit time. GPP determines the total amount of initial energy and material entering terrestrial ecosystems. It reflects the vegetation productivity of terrestrial ecosystems, which profoundly influences the carbon cycle and global climate change. The Tibetan Plateau (TP), with an average elevation of more than 4000 m, is often called the \"third pole\" [4]. Previous studies showed that 49% of streamflow in the YellowRiver, 15% of streamflow in the Lancang-Mekong River, and a considerable amount of streamflow in the Yangtze River originate from the TP [5], and the TP is often regarded as the \"Asian water tower\". With a large vertical height difference, rich landform types, diverse climatic environments, and complex ecosystem environments, the TP is a vital ecological barrier for China. Due to its unique geographical location, the TP is also a sensitive area of East Asia [6; 7; 8]. In particular, the TP shows a significant warming trend and increased precipitation [9; 10]. The climate conditions on the TP are extremely variable, and its ecosystem pattern can easily be altered by external disturbances [11]. Land surface observation systems, mainly based on field observation and satellite remote sensing products, are critical for studying changes in land surface variables. With the development of global satellite remote sensing technology, vegetation index products retrieved by satellite remote sensing, such as the leaf area index (LAI), the normalized difference vegetation index (NDVI), and gross primary productivity (GPP), have provided effective support for studying global large-scale vegetation changes. However, due to the high elevation and harsh natural environment, there are few observation sites at high elevation on the TP, especially soil moisture and carbon flux observation sites [12; 13]. Affected by high elevation, cloud cover, and auxiliary data, it is doubtful whether remote sensing products can provide a long-term stable vegetation index. Previous studies that included observations, remote sensing products, and simulations have been carried out for carbon flux and its responses to climate change on the TP and China [4; 11; 14; 15; 16; 17; 18]. Empirical models, remote sensing products, and numerical simulations are effective ways to estimate regional and global vegetation distributions. The next generation of land surface models includes the traditional hydrothermal transfer process, biogeochemical processes, and dynamic vegetation [19]. Dynamic global vegetation models (DGVMs) have been coupled with climate models to study the response of vegetation to climate change. In contrast to models of the fixed-satellite vegetation type, some vegetation characteristic parameters in DGVMs are calculated based on temperature, precipitation, and radiation; thus, the vegetation coverage simulated by DGVMs varies with climate change. DGVMs include: (1) simple biogeography rules to delineate the vegetation types based on climate; (2) carbon and nitrogen cycle modules; (3) vegetation dynamics modules. Current DGVMs include the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ DGVM), the Integrated Biosphere Simulator Model (IBIS), the BIOME4 model, the CLM-DGVM, and the Institute of Atmospheric Physics Dynamic Global Vegetation Model (IAP-DGVM). The LPJ model generally describes the temporal and spatial characteristics of regional vegetation, and it has effectively simulated the responses of vegetation patterns and functions to climate change [20].