Considering spatial heterogeneity of LAI and nonlinearity of its inversion model, a new spatial scaling method based on Gaussian distribution theory was proposed, aiming to quantitatively analyze scale effects and reveal scaling rules. In this method, higher spatial resolution data, obeying Gaussian distribution when the volume is large enough, were taken as baseline. Statistical parameters and Gaussian distribution forms were integrated into the process of spatial scaling. Barley was selected as experimental object. Firstly, multi-resolution data at 10m, 15m, 20m and 30m were constructed based on 5m resolution data through up-scaling algorithms. Secondly, extracting statistics of these data and constructing scaling data based on Gaussian distribution theory. Finally, quantitatively analyzing scale effects of LAI by introducing sensitive bands ordering and multivariate linear regression. Number of effective bands, R2 of observations and estimations, and MRA could fully confirm the feasibility and validity of this proposed method.