The quality of remote sensing Leaf Area Index（ LAI） products and the uncertainties of the products are evaluated with ground measurements. The spatial scale mismatch problem of these two data should be solved through upscaling before c omparing these two dataset. To evaluate remote sensing LAI products using ground measurements

the spatial scale mismatch problem of these two datasets should be solved first. A new approach based on Taylor Series Expansion Model（ TSM） was p roposed in this paper. It combines the information of high-resolution images

NDVI-LAI empirical model and the LAI ground measurements to generate the upscaled LAI at the coarse-resolution scale. This approach not only can upscale ground measured LAI to coarse-resolution

it also can provide upscaling accuracy for each pixel. The LAI measurements collected in 2008 in the Heihe experimental research region was used to test this method. The possible error associated with this method is from two sources. One is neglection of the third- and higher-order TSM terms

which can be estimated using Taylor series remainder. Another is the uncertainty of the empirical model. The total error of the upscaling method is the sum of these two errors. The data used in this study were collected during the Watershed Allied Telemetry Experimental Research（ WATER） project. The ground measurements were collected in the Yinke Oasis and the Huazhaizi Desert experimental area using LAI-2000

TRAC

fisheye camera

and by manual measurement. The corn LAI dataset was chosen for analysis. Airborne CCD and ASTER data were used to help upscaling to ground measurments. Upscaled LAI ground measurements were compared with NDVI-LAI empirical model calculated LAI and MODIS

GLASS LAI product at 1km scale. The accuracy of the upscaling process was used as reference to select the upscaled LAI. Empirical model c alculated LAI is usually considered as more reliable validation data

but it’s not directly associated with ground measured LAI

and can’t provide accuracy of each pixel. Comparison with empirical model calculated LAI shows that this approach can provide reliable result. Also the accuracy of this approach is a good indicator for selecting validation data. By using the upscaled LAI with a cceptable accuracy only

comparison with MODIS and GLASS LAI products show that both products are lower than ground measured LAI at this region.In comparison with other validation method

this method can improve the representativeness of ground measurements by combining more information at the sub-pixel scale and considering the heterogeneity of the land surface. Consequently

this method is suitable for validation studies in which the field-measured data are derived from non-u niform surfaces at coarse-resolution pixel scales. It’s a new approach to upscale ground measurements for validation of coarse-r esolution products. Mostly important is that the upscaling accuary can be estimated for each pixel

which can provide a reference of how to select high quality ground measurements for validation. Though comparison with MODIS and GLASS LAI products in Heihe region shows both products are underestimating LAI

but this conclusion is not suitable at global scale.