## 1 Introduction
That is to say, different spatial processes may operate at different scales, and thus conclusions based on one scale may not be applicable to another scale. Again, spatial patterns are also scale dependent. Spatial patterns may look like a cluster at one scale but random at another [13]. Therefore, remote sensing can be thought of as a particular case of the MAUP [2]. Model application should be carefully dealt with rather than data interpretation. Retrieval models used in remote sensing are usually developed at a local scale, implying that models are merely suitable when the medium where the process takes place is homogeneous [4]. If these models are directly applied to a large scale, scale effects may be generated. In addition, the dominant factors are not the same at different scales, and they may lead to different retrieval models for different scales. Depending upon the observation scale, the process which appears homogeneous at a local scale may become heterogeneous at a large scale and parameters and factors which are important at one scale may become trivial at another [3,14,15]. For example, in the research on heat-water exchange, the turbulence flux that can be ignored at the large scale has to be taken into account at the local scale. Furthermore, the action of coupling different types of models makes the problem more severe. Atmospheric processes often occur at larger spatial scales and smaller temporal scales than hydrological processes [5,16]. How to couple these two types of models is not a simple question to answer. Without thorough consideration, using data and models at an inappropriate scale could lead to a meaningless conclusion [13]. To scientists, scale is undoubtedly one of the most important bases of research. What we study cannot be disengaged from the scale. [PERSON] and [PERSON] [1] pointed out that scale is important because it not only defines the limits of our observations of Earth, but also is often a parameter in the physical and social processes which shape geographic phenomena. Scale research is also an important step in the campaign of validation of remote sensing products [17]. As spatial resolution becomes coarser, a scaling method must be developed in order to determine the accuracy of retrieval products [18]. Furthermore, the scale may determine the reliability of the research. From microscopic and macroscopic scales, we can understand both details and trends. It is helpful to thoroughly interpret geographic phenomena. In addition, the scale may determine the cost of research because detailed data is generally more expensive to measure, process and analyze. Undoubtedly, the development of scale research can relate the data measured at different scales and make use of this data effectively. It will solve the problems discussed above and drive the evolution of other relevant disciplines. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The next sections will be organized as follows. In Section 2, some basic definitions will be given, as researchers may not seem to have agreed on the meaning of such concepts. They are the basis of analysis the scale issues. In Section 3, the mechanism of scale effects, which may benefit the scaling model, is briefly discussed. The main causes of scale effects and the scaling effects of the measurements, retrieval models and products will be reviewed and discussed. These are the key points in resolving scale issues in remote sensing. If the scale effects could be estimated correctly, the scaling model would become easier. One of the great advantages of remote sensing is its capacity for providing data at multi-scales, which is increasingly used to evaluate the influence of scale effects for identifying structure or patternsand modeling results. This raises a problem: where is the interval in which the phenomenon or the structures are nearly invariable or slowly variable?