Contemporary remote sensing science is challenged by serious supply-demand contradiction between data acquisition and application capabilities. Scale issue is attributed as a major and essential problem retarding the development of remote sensing in both theories and applications. Due to heterogeneity and complexity of land surface

scale issue naturally arises as how remote sens-ing observations match phenomenon or process on earth surface or how multi-scale remote sensing observations maintain c onsistency. Therefore

this paper endeavors to build up a universal scaling methodology framework

which would hopefully help interpret and transfer remote sensing products between sensor-defined scales and user-demand scales.To begin with

scale issues in remote sensing are analyzed as: inconsistency between pixel-based remote sensing observation and conventional point-based field measurement

inconsistency between remote sensing products at varied scales

scale adaptabilities of physically-based models and scale inconsistency between remote sensing product and user demand. It is pointed out that multi-scale remote sensing information is related to characteristics of land surface and simple averaging or classic image processing will result unexplainable data.Second

scaling methods are explored in terms of up-scaling and down-scaling. Up-scaling involves scaling from point-based field observations to pixel-based remote sensing observations and scaling between remote sensing products at varied scales. For the former a general way is to introduce extra information while the latter always uses two approaches as clumped or distributed c omputation. Preferred up-scaling is supposed to reserve key information

but most current methods tend to derive more homogeneous product of low information quantity. Down-scaling refers to derive data with more detailed information. The insufficiency of information of the original image is unavoidable and it is mandatory to introduce extra information. The earlier downscaling methods largely rely on pre-defined assumptions or interpolation

while a recent trend is to use relevant geographic features as prior knowledge. The central issue in scaling is concluded as how to reserve key information or how to introduce extra information.Third

borrowing ideas of prior knowledge from quantitative remote sensing retrieval and trend surface in geographic applications

the paper proposes the concept of geographic trend surface and establishes a universal scaling methodology. The core is:（ 1） to construct prior knowledge warehouse by integrating all necessary information in retrieval and scaling

including link model

spectral knowledge

geographic trend surface

recent temporal change records and process models;（ 2） to adjust the geographic trend surface in spatial and temporal frame under the support of prior knowledge warehouse. All kinds of geographic information

including temporally stable geographic features

temporal change records

field observations and process models

are used to derive the spatial patterns of the target feature

based on which the spatial representativeness of observation at each location can be identified;（ 3） to update prior trend surface to post trend surface through Bayes theorem

where prior trend surface is expressed as prior knowledge and new remote sensing observations are taken as new knowledge;（ 4） to derive the product of land surface parameter at demanded scales based on error analysis and then update the post trend surface into prior knowledge warehouse. As a whole

c onstruction of geographic trend surface lays the foundation of the methodology

which aims at addressing problems of information insufficiency or key information reservation in scaling. The paper provides a conceptual scaling model

while many technical details need to be fulfilled further in practice.