Spatial scaling for net primary productivity（NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions.How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem.Nonlinearity and effects from land cover type are two main problems in NPP scaling.In this paper

the contextural approach based on mixed pixels and support vector machine（SVM） algorithm are used to make the scaling model from the fine resolution（TM） to the coarse resolution（MODIS）.Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed

and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished.The result shows that the correlation between Rj

c

orrected of the correction factor for scale effect and 1-Fmiddle density grassland estimated by SVM regression model is higher（R2=0.81）.Before the correction for scale effect

the correlation between NPPMODIS and NPPTM is lower（R2=0.69;RMSE=3.47）

while the correlation between NPPTM and corrected NPPMODIS

c

orrected is higher（R2=0.84;RMSE=1.87）.Therefore

NPP corrected for scale effect has been greatly improved in both correlation and error.