## 1 Introduction  
The advance of remote sensing technology in the 20 th century has provided a powerful means to conduct regional and global measurements. Remote sensing technology can quickly access a wide range of real-time land surface spatial information and provides an effective way for resource surveys, environmental monitoring and disaster prediction. With the help of remote sensing technology, one can get geo-information quickly, accurately, efficiently and comprehensively. Undoubtedly, remote sensing will play an increasingly important role in the field of geosciences. At present, remote sensing technology has entered an era of quantitative analysis. Thus, the important issues - scale effects and scaling - have already become one of the most important research focuses of remote sensing [1, 2]. The scale represents the window of perception [2], the ability of observation, and reflects the limitation of knowledge through which a phenomenon may be viewed or perceived [1]. Consequently, scale issues should be carefully dealt with in remote sensing [3]. On the one hand, most retrieval models and algorithms are basically derived at small scales, implying that the land surface is homogeneous. If the models and algorithms are used at large scales, they may produce certain errors [4]. On the other hand, geo-information which is closely related to human cognition has the concept of regional scale, for example, weather forecasting, environmental monitoring, crop growth and yield estimation, disaster assessment, resource survey and so on. In short, the description of a process must be expressed at a certain scale. For instance, soil moisture content that is estimated from a small piece of farmland cannot be used as a drought index for large-scale agricultural drought monitoring. In other words, small scale information cannot be used as a substitute for regional scale information. The discrepancy between observation scale, model scale and land surface process scale may lead to different conclusions in the processes of monitoring and forecasting. As a result, the scaling of geo-information is inevitable for many disciplines. Scale effects constrain the accuracy of retrieval and limit the development of remote sensing applications. Consequently, they are becoming emerging problems which attract more and more the attention of scientists. Firstly, in order to analyze the scale issues of remote sensing, the problems of scale and the importance of scale will be reviewed and discussed. As we know, research about scale has already permeated various disciplines, such as hydrology [5-7], meteorology [8], ecology [9, 10] and geography [1, 2, 11]. The reasons why we need to transfer information across scales are simple. The instrument is one of the reasons; different instruments have different instantaneous fields of view that correspond to different spatial resolutions. In addition, the scale at which the available data has been measured is usually different from the scale required by the models. Again, the scale at which these models operate often varies from model to model. Furthermore, several factors, such as manpower, finances, time and other resources, constrain the choice of scale. For example, running a general circulation model will take a long time, even with a high-performance computer. Under the limitation of resources, a larger scale may be preferable. Finally, the scale of model output may not be in coincidence with the policy scale at which the decision is made. Taking into account these factors, the reason why more attention has been paid to scale issues, is easy to be understood. Obviously, research is suffering from a severe scale discrepancy between data sources and the models used. Both data interpretation and model application become difficult due to scale issues. [PERSON] [12] proposed the Modifiable Areal Unit Problem (MAUP), which means the results of studies could be different using different areal units and is thought of as the combined output of a scale problem and an aggregation problem.