Remote sensing imagery provides vast information about the land surface

and the spatial distribution of land cover types by classification could be obtained.Moreover

the spectrum of land surface objects is useful to improve the accuracy of image classification.However

the spectrum of the same object may be different when they are measured at different measuring scale and with different method.For example

the spectrum of winter-wheat extracted from Landsat TM and measured in the field are different.So it is important to study the scale effect and scaling method on the spectrum at different measuring scale.In this paper

we took the winter wheat as example

and selected Shunyi region in Beijing as our study area.Firstly the definition of three-scale spectrum was explained

then we analyzed the discrimination using the measuring data to highlight the importance of the scale transformation of spectrum.The collected data included: field measured spectrum of leaf and canopy and the hyper-spectrum high-resolution remote sensed imagery OMIS

Landsat TM and MODIS data.We compared the winter-wheat spectrums and calculated the slope of "red-edge" at different measuring scales.We also studied the scaling-up method of the spectrum

and the physical model（SAILH） and statistical model（Linear mixing model） were used to describe the relationship between the spectrum at different measuring scales.SAILH is a typical radiation transfer model

which can be used to simulate the canopy spectrum by taking the leaf spectrum

some structural parameters and environmental variables as inputs.In this experiment

the input parameters were acquired with high accuracy

so the error of simulation result is very small: 8.45%.Linear mixing model was used to describe the relationship between endmember spectrum and pixel spectrum.The resolution of MODIS imagery（visible and infrared bands） is 250m

which was taken as pixel spectrum

and the endmember one can be got by multiple methods

here we adopt two methods: Broadman method from MODIS imagery and aggregation one from TM imagery.The unmixing results were compared and analyzed

and the linear mixing model was validated.Through the spectrum data in the study area

the winter-wheat spectrum of leaf

canopy and OMIS imagery is different and the character reflecting the plant growing status is also varied.As for the scaling method

we found statistical models and physical models are fit on the three research scales respectively.However

the endmember selecting method from the imagery also needs more improvement

and more statistical models or coupling physical models should be explored in the further work.