Abstract: Optical remote sensing is still one of the most attractive choices for obtaining leaf area index (LAI) information, but currently may be derived from remotely sensed data with limited accuracy. Effective leaf area index (LAIe) of montane evergreen broad-leaved forest  in southwest Sichuan was inventoried and assessed in 83 sample field plots of 20 m×20 m using different types of image processing techniques, including simple spectral band, simple spectral band ratios and principal component. Texture information was extracted by gray level co-occurrence matrices (GLCM) from different types of processing image. The results showed that there were correlations of different degrees between LAIe and texture parameters, and highly significant correlations were observed between LAIe with the homogeneity of the B₁ band, B₁/B₄ band ratio or principal component PC₁. Using texture information of remotely sensed data as auxiliary variables, we developed geostatistics models. Compared with the model based on NDVI auxiliary variable, the accuracy of LAIe were improved, presenting an increase by 5.3% with the homogeneity of the B₁ band, 11.0% with the homogeneity B₁/B₄ band ratio, and 14.5% with the homogeneity principal component PC₁, and the statistical errors were also reduced to some extent. The optimal LAIe model of spatial geostatistics was obtained when taking NDVI and homogeneity principal component PC₁ as auxiliary variables (R²＝0.840，RMSE=0.212). Our results provided a new way to estimate regional spatial distribution of LAI using other auxiliary variables besides the vegetation index.