Long time series Global LAnd Surface Satellite（GLASS） Leaf Area Index（LAI） product（1981—2012） is generated from time-series MODIS and AVHRR reflectance data using general regression neural network method. This study assesses the performance of the GLASS LAI product in two ways:（1） by comparing the spatial and temporal characteristics of the GLASS LAI product with those of other moderate-resolution LAI global products and（2） by comparing the GLASS LAI values with ground measurement data. The results show that the GLASS LAI product achieved a good consistency with the MODIS and CYCLOPES LAI products at the global scale

although with differences in magnitude of LAI values. The largest differences occur between the GLASS and CCRS LAI products in high northern latitudes and close to the equator

followed by differences between the GLASS and MODIS LAI products and the GLASS and CYCLOPES LAI products. The GLASS and MODIS LAI products have more complete temporal trajectories than the CYCLOPES LAI product

while the GLASS and CYCLOPES LAI products have more continuous and realistic trajectories than the MODIS LAI product. The GLASS LAI product maintains reasonable profiles in c ontrast to the MODIS LAI product

which shows dramatic fluctuations

particularly during the growing seasons. Compared with 20 ground-measured LAI reference maps at 17 sites

the GLASS LAI product shows lower uncertainty

with an R-square of 0. 76 and RMSE of 0. 51

than the MODIS and CYCLOPES LAI products.