The science teams of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) have been supporting various global climate, biogeochemistry, and energy flux research efforts by producing valuable long-term Leaf Area Index (LAI) products. Although intensive LAI validation studies have been carried out globally, retrieval accuracy has been assessed only over short time spans and product quality is reported as a constant based on the assumption that it is stationary. However, a preliminary evaluation found a time-dependent signal degradation of the MODIS sensors which may cause the uncertainty of the Bidirectional Reflectance Factor (BRF) product to exceed the preset uncertainty buffer in the retrieval algorithm and result in MODIS LAI quality variation and time-series inconsistency with VIIRS LAI. Therefore, to ensure the reliability of trend-detection studies and to answer the critical question of whether the algorithm configuration is still suitable for the uncertainty level of present BRF product, there is a need for a more comprehensive investigation regarding the performance stability of LAI retrieval algorithm due to model uncertainty and input uncertainties. This paper reports analyses of inter-annual stability and trends of LAI uncertainties (inferred from product quality flag rather than ground-based validation) as well as LAI magnitudes using multi-year (MODIS: 2001–2019, VIIRS: 2013–2019) and multi-site (445 sites) datasets. We found that the quality metrics of the two products are consistent across different biome types and LAI values (R2 ranged from 0.88 to 0.99). In the 19-year MODIS period, we found a significant increasing trend in LAI magnitudes (4.5%/decade) which agrees with previous reports, while there was no significant trend in product quality. Moreover, quality metrics showed much smaller inter-annual variations than LAI values, which confirms the performance stability of retrieval algorithms and suggests that the configuration of MODIS algorithm is still compatible with the uncertainty of algorithm inputs. Additionally, this study provides support to the “Greening Asia” studies by eliminating the possibility that the “greening” could be attributed to artificial trends caused by sensor degradation. We also found very small inter-annual variations in atmospheric conditions (e.g., cloud and aerosols), which further assures that the performance stability of retrieval algorithm. Overall, our results demonstrate the robustness of the retrieval algorithm in the presence of changes in input uncertainties. The temporal stability of the algorithm performance indirectly strengthen the confidence in the continuous use of MODIS LAI products either independently or in combination with its successor - VIIRS LAI - to study global and regional vegetation dynamics