Various remote sensing sensors observe the Earth's surface at different spatial resolutions. Due to the spatial heterogeneity and model's nonlinearity, there would be some scale difference among different remote sensing surface parameter(such as leaf area index, LAI)derived from remote sensing images with different resolution. In this paper, the spatial scale effects and transformation methods are studied using both experiment at Xilinhaote steppe region and theoretic models. Firstly, different upscaling methods were presented to simulate the scale effects between fine resolution and coarse resolution. Secondly, Taylor expansion was conducted for both NDVI model and reflectance model for LAI estimation, and the nonlinearity can be well explained by the second derivatives. The scaling difference was reduced from 5.6% to 1.45% and 0.78%, respectively, if the contributions of the second derivatives were corrected for LAI models based on NDVI and reflectances of red and NIR bands. Finally, the effects of the nonlinearity and heterogeneity on scaling are quantified. It can be observed:(1)the scaling error increases with the vegetation coverage under same spatial heterogeneity;(2)the heterogeneity in red band is about 100 times sensitive to scale error than it in near-infrared band for high NDVI conditions;(3)for terrestrial vegetation region, the LAI would be underestimated at coarse resolution. The nonlinearity of the exponent LAI model based on NDVI is the primary factor, and the nonlinearity of NDVI variable contributes about 23.5% scaling difference;(4)for wetland region(mixed by vegetation and water), the LAI would be overestimated at coarse resolution. The nonlinearity of NDVI variable becomes the dominant factor, and the scaling difference can still be corrected by the contribution of the second derivates of the LAI model based on reflectances of red and NIR bands. Therefore, we developed a series methods and models to quantify the scale effect of LAI, and the scaling error was consistent with contributions of the second derivates by Taylor expansion, which can also be applied to other surface parameters.