Leaf area index（LAI） is an important bio-physical character of vegetation and can be effectively achieved through remote sensing technology.However the LAI inversion from low resolution data induces a scaling bias due to the heterogeneous of the surface and model non-linearity

which may cause the scale effect on the LAI estimate.In this work

the Yingke oasis of Heihe River is selected as the study area.Based on Hyperion data

a two-layer canopy reflectance model（ACRM） is introduced to calculate LAI.The low resolution LAI are then achieved in two ways:LAImean

the mean of LAI

is directly calculated from Hyperion;and the LAIp is computed from linear cumulative Hyperion data.Statistics shows that there is a serious underestimation of LAIp.On the basis of LAI-NDVI regresion equation

the Taylor Mean Value Theorem is applied to creat an error factor and to conduct scaling error correction.The result of error correction（LAIr） has a high relationship with LAImean

which shows that the method is effective and suitable for scale effect correction and can be used to correct other LAI product

such as MODIS LAI.Finally

the causes for scaling bias are discussed.It is found that the spatial heterogeneous is the key factor which may lead to the error in LAI inversion.