The Tibetan Plateau serves as an ecological security barrier for China. Under the influence of climate change and human activities, the vegetation in the Tibetan Plateau has varied substantially, trending overall toward “greening”. However, previous studies on global-scale vegetation change have reported substantial uncertainties in both the magnitude and trend of current Leaf Area Index (LAI) derived from different satellite remote sensing products. Therefore, addressing the uncertainty in spatial and temporal vegetation patterns in the Tibetan Plateau is not only crucial for scientifically assessing how alpine ecosystems respond to climate change, but also provides a reference for reducing uncertainties in future Earth Observation satellites. Therefore, by focusing on the river source region of the eastern Tibetan Plateau with relatively favorable vegetation conditions, this study aims to elucidate the differences in the magnitude and the trend of LAI among three long-term LAI data products (GIMMS, GLASS, and GLOBMAP) over the past few decades. In terms of multi-year averages, the relative uncertainty among the three LAI products in the study region reached 26.19%. Among the three periods of the growing season, the early-growing season exhibited the highest relative uncertainty at 32.7%. From 1982 to 2018, GLOBMAP and GLASS showed that LAI has increased by 25.05% and 20.24%, respectively, whereas GIMMS indicated a somewhat marginal change of only 3.85% in the growing season LAI, suggesting that the “greening” phenomenon is most prominent in the former two products. Three products showed different trend directions for LAI during the entire growing season, early-growing season, and mid-growing season periods in about 60% of the study area, with this percentage exceeding 75% during the late-growing season. Among the land cover types, the steppe had the largest relative uncertainty of 37.7%, while the forest had the largest proportion of inconsistency in the direction of trends in LAI, accounting for over 60% of forested area. In the absence of large-scale ground-truth LAI measurements, while the LAI product that has the highest accuracy remains unknown, the implication from the present study is the caution should be exercised in relying on any single LAI product when investigating the effects of vegetation change on ecosystems, hydrology, and climate, as this may lead to contrasting conclusions. Admittedly, with future advancements in high-resolution satellite observations and artificial intelligence models, the uncertainty in LAI products is expected to further decrease. Achieving this will require a community effort across multiple disciplines, including remote sensing, surveying, ecology, and geography.