 This article describes the development and evaluation of 30-year-long datasets of leaf area index, LA, and fraction of photosynthetically active radiation absorbed by vegetation, FPR. These datasets are critical for monitoring global vegetation dynamics and modeling exchanges of energy, mass, and momentum between the land surface and planetary boundary layer. A neural network algorithm was developed to generate corresponding LAI-3G and FPR-3G datasets with 15-day temporal frequency, 112-degree spatial resolution, and a temporal span of July 1981 to December 2011. The quality of these datasets was assessed through comparisons with field measurements, existing alternate satellite database products, plant growth limiting climatic variables in the northern latitudes and tropical regions, and correlations of dominant modes of inter-annual variability with large-scale circulation anomalies such as the EINENO-Southern Oscillation and Arctic Oscillation. The utility of these datasets was documented by comparing seasonal profiles of LAI-3G with profiles from 18 state-of-the-art Earth system models, which consistently overestimated satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes. These datasets can be obtained freely from the NASA Earth Exchange NEX website. This article was authored by Ranga B. Minini, Sholong Pyao, Ramakrishna Arnamani, and others. We are article.tv, links in the description below.