With the development of quantitative remote sensing, the scaling problems attractmore and more attention. The iscrepancy between observation scale, model scale and land surface process scalemay lead to different conclusions. Now, how to ffectively scale remotely sensed information atdifferentscales already becomesone of themost importantresearch focusesofremote ensing. The aim ofourresearch is to compare and analyze two general scalingmethods,the Taylor Series Expansion Model(TSM)nd the Computational Geometry Model(CGM), and apply them to the scaling of leafarea index (LAI). Firstly, the necessity and portance of scaling are analyzed. Secondly, based on the research of description for the same objectusing different scale data,the echanism of scaling effects is presented. Then, the two general models, TSM and CGM, are briefly introduced and their dvantages and disadvantages are discussed in detai.l Finally, through the retrieval of leaf area index, the two models are omprehensively compared and analyzed in threedistinct landscapes. The resultshows thatthe relative scaling error increaseswith the eterogeneity of land surface. The relative scaling error is 2% in the relatively homogeneouswoodland; however, itarises up to7% ncrop-watermixed areas. Apparently, theTSM can bettercharacterize the scale effectand obtainmore accurate surface parameters hen both small scale (high resolution) data and large scale (low resolution)data are available.The relative scaling error can be educed to less than1% forall these test landscapes when TSM is used in scaling. In contras,t CGM can notproduce rational result nd the relative error is still large. Itmay be due to using inappropriateweights ordata ranges in themode.l More study about CGM sneeded.On thewhole, it isnecessary to selectthe suitable scaling modelaccording to the practical applications. The scalingmakes he remote sensing products atdifferentscales comparable and the surface parameterretrievalresultsmore accurate.Scaling technique illprovide a powerful technical support for applications in resources survey, environment and disaster monitoring, and other elevant fields.