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The leaf area index (LAI) is a critical variable used to characterize the terrestrial ecosystem and model land surface processes. Remote sensing is an ideal tool for mapping the LAI. However, the quality of current satellite LAI products does not meet the requirements of the user community in terms of estimation accuracy and data consistency. One way to address these issues is to develop LAI integration algorithms that incorporate existing multiple LAI products and prior knowledge. This paper presents a new data integration method based on empirical orthogonal function (EOF) analysis. The proposed EOF integration algorithm can be operated on both fine and coarse spatial resolution to accommodate the problems arising from a large volume of data. Two runs of multivariate EOF analysis are proposed to address the issue of incompatible temporal resolutions among different data sets. Comparisons with high-spatial-resolution LAI reference maps at 12 sites over North America show that the proposed method can improve LAI product accuracy. After data integration, R2 increases from 0.75 to 0.81 and the root mean square error (rmse) decreases from 1.04 to 0.71 over moderate-resolution imaging spectroradiometer (MODIS) products. The improvement of R2 and rmse over Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products is not as significant as that over MODIS products. However, the use of a combination of multiple data sources reduces the bias of the LAI estimate from MODIS's 0.3 and CYCLOPES's -0.2 to -0.1.