This paper focuses on Leaf Area index (LAI) inversion, using EO-1 Hyperion data for Huangfengqiao forest farm, YouXian County, Hunan Province. First, LAI was acquired using a LAI-2000 canopy analyzer at 130 sample plots (60 m × 60 m), with a Global Positioning System (Trimble GPS Geo XT). Second, atmospheric correction was applied to Hyperion data using the ground-synchronous canopy observation data. Third, effective vegetation indexes were selected to estimate LAI, according to research on correlation between LAI, bands and vegetation indexes derived from Hyperion imagery. Finally, an optimal estimation model of LAI was built by curve estimation, stepwise regression, and a partial least-squares regression algorithm. Results show that sensitivity of ratio vegetation index (RVI) was highest among all model factors, followed by SARVI_0.1, NDVI₇₀₅, NDVI, SARVI_0.1, and SARVI_0.25. Among all fit models, the effect of the partial least-squares regression was best, with R² coefficient 0.84, whereas the curve estimation effect was worst, with R² coefficient 0.64. Model precision analysis shows that it is reliable to build the model using 5 to 6 independent variables, and prediction accuracy of the partial least-square regression was the greatest.