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As an important ecological parameter in land surface processes, Leaf Area Index (LAI) and its inversion with remotely sensed data are hot topics in quantitative remote sensing field either domestically or internationally. Current approaches for estimating LAI from optical remotely sensed data are classified into several categories: 1) the empirical relationship of LAI and vegetation indices (VI); 2) inversion of a radiative transfer (RT) model; 3) lookup table (LUT) method; and 4) neural network (NN) algorithms. Approach in category 1) is empirical therefore location specific. Retrieving LAI with approach category 2) is physically based, but hampered by the fact that the inverse problem is ill-posed, which leads to unstable and often inaccurate results. In this study, we examined three LAI retrieval schemes as 1), 2) and 4) to retrieve LAI from Landsat ETM+ imagery in two typical experimental sites, one located in the arid, semi-arid high altitude Heihe River Basin, northwestern China, and another a humid temperate mountainous hilly region of the Hanjiang River Basin, middle western China, where comprehensive and extensive field campaigns for measuring LAI with LAI-2000 and TRAC have been successively conducted in summer seasons from 2002 to 2006. With the data obtained in field seasons of these successive years, a scale transferring scheme was developed to convert the Landsat TM LAI map to compare with MODIS LAI product available to public on internet by NASA, it was found that the MODIS LAI were underestimated about 56-71% in the arid, semi-arid Heihe region, while it was underestimated about 10-21% in general in the humid and temperate Hanjiang region.