 This article describes the development and evaluation of 30-year-long data sets of leaf area index, LAI, and fraction of photosynthetically active radiation absorbed by vegetation, F-PAR, using a neural network algorithm. The data sets were generated from improved third-generation global inventory modeling and mapping studies, GEMLIS, Normalized Difference Vegetation Index, NDVI-3G, and Terra Moderate Resolution Imaging Spectror Radiometer, MODIS, LAI, and F-PAR products for the overlapping period 2000-2009. The quality of these data sets was assessed through comparisons with field measurements, existing alternate satellite data-based products, plant growth limiting climatic variables in different regions, and correlations with large-scale circulation anomalies such as the EI-Nino-Southern Oscillation and Arctic Oscillation. The results showed that these data sets were suitable for research use in other disciplines, and their utility was documented by comparing seasonal profiles of LAI-3G with profiles from 18 state-of-the-art earth system models. These data sets can be obtained freely from the NASA Earth Exchange, NEX, website. This article is authored by Ronda Biminini, Sharlan Pyao, Ramakrishna Arnamani, and others.