Thus, reliable information about LSP and spatiotemporal dynamics of vegetation is significant to comprehend carbon budgets and climate change from regional to global scales. Since the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor launched in the 2000s, vegetation indices products (e.g., Normalized Difference Vegetation Index, NDVI) have been produced and widely used in the long-term vegetation tendency analysis [4; 5], detecting interactions between vegetation dynamics and climate changes [5], and carrying out phenological retrieval studies at global or regional scales [6; 7; 8]. Nevertheless, the vegetation indices products often saturate during peak growing season and are susceptible to background soil information [9]. Compared with vegetation indices, satellite-derived biophysical products, namely leaf area index (LAI) [10; 11], fractional vegetation cover (FVC) [12; 13; 14], and gross primary production (GPP) [15], offer new potential for vegetation dynamics monitoring at global or regional scales. LAI is defined as half of the total green leaf area (i.e., photosynthetic active leaf area) per unit of the land surface area [16]. FVC represents the ratio of green vegetation projected vertically onto the ground surface [13]. GPP refers to the total amount of carbon fixed per unit of space and time during the process of green vegetation photosynthesis [17]. As excellent proxies for vegetation growth status, these biophysical products are more closely related to photosynthesis and vegetation transpiration and are important factors when depicting energy exchange, material, and carbon cycling between the Earth and the atmosphere [18; 19]. Reasonable and reliable interpretation of terrestrial ecosystem trends is the primary issue which these satellite products should address. The synergistic application of multi-source biophysical products to study terrestrial vegetation dynamics and seasonal variations at global scales requires comprehensive analyses of the various products. However, the long-term vegetation dynamic characteristics and phenological information inferred from different products vary considerably across regions, due to the regional climatic stress, land cover, and topographic complexity [3]. [PERSON] et al. [20] pointed out a much smaller rise in global GPP when compared with the growth rate of global vegetation LAI mainly caused by warming-induced drying. A survey in South Asia also showed that large areas of surface vegetation greening represented by LAI did not bring proportional increases of carbon uptake (i.e., GPP increase) [21]. In addition, the difference in data quality among different sensors results in high inconsistencies in the detection of LSP metrics on the global scale [22]. This was also confirmed in a study conducted in China, where their study further highlighted the high mismatches of LSP derived from vegetation indices between those from LAI in the areas with arid or high altitudes (e.g., Qinghai-Tibet Plateau) [23]. The above studies pose a challenge for monitoring surface vegetation dynamics or LSP at large spatiotemporal scales, depending on satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. The Three-River Source Region (TRSR), as the birthplace of the Yangtze, Yellow, and Lancang River in China, is one of the most eco-environmentally vulnerable areas in the world due to its complicated land surface, high elevation, spatial heterogeneity, and complex hydrological responses to variable climate conditions [24; 25; 26; 27; 28]. Long time-series satellite products are necessary to understand the vegetation growth and make sustainable development strategies in this region. However, most previous studies only use a single remote sensing product to reveal the vegetation dynamics or LSP over TRSR. They do not provide a comprehensive analysis of multiple biophysical products to reveal spatiotemporal variability of vegetation dynamics and LSP on the various surface characteristics.