Abstract: Abstract: Determination of crops' leaf area index (LAI) is of great significance for growth monitoring, water-fertilizer regulation and yield assessment. For the sake of providing basic data for wheat field management, estimation of LAI value of wheat canopy was conducted by using hyperspectral indices. The optimization of Soil-adjusted Vegetation Index（OSAVI）which is the strongest correlation with LAI was selected from 16 kinds of existing hyperspectral indices like GREEN-NDVI and 5 kinds of newest established hyperspectral indices like FD730, and linear model for wheat LAI inversion was established by adopting the Least Squares Method algorithm. The analysis results showed that the calibration set decision coefficient (C-R2) and prediction set decision coefficient (P-R2) of the model reached 0.832 and 0.825 respectively, the Root Mean Square Error of Calibration set (RMSEC) and the Root Mean Square Error of Prediction set (RMSEP) were 0.478 and 0.461 correspondingly, so the accurate inversion of wheat LAI could been realized. To further improve inversion precision, the model was optimized by using the Least Squares Support Vector Regression (LS-SVR). In comparison with linear model, the coefficients of C-R2 and P-R2 reached 0.851 and 0.848 respectively, obviously, higher than the ones of linear model. In the meantime, RMSEC and RMSEP were 0.467 and 0.441 correspondingly, lower than the ones of linear model. The facts also demonstrated that the LS-SVR model was better than linear model for inversion. In order to analyze prediction ability of OSVAI with regard to different LAI samples, comparative analysis was implemented between OSVAL index and the indices such as GREEN-NDVI. The results indicated that OSVAI model built had good prediction ability for the higher LAI value samples and the lower LAI value samples, and meanwhile it could also avoid influencing the result of estimation by the canopy density effectively. Finally, remote sensing thematic map of wheat LAI was achieved by using the LS-SVR model with the OMIS images. By comparing the map result with the ground measurement, the R2 value of fitting model was 0.774, the RMSE was only 0.476, which proved that higher similarity existed in the two sets of data. The results indicated that wheat canopy LAI information could be acquired accurately by using hyperspectral indices, and OSAVI was optimal index for inversion modeling, LS-SVR was the optimization algorithm for modeling. The study can provide a reference for crops growth assessment such as wheat.