Scale effect is a basic scientific problem of quantitative remote sensing. This review is focused on up-scaling research from these four aspects: the description of scaling phenomenon, the causes of scale effects, approaches for scale transformation, and the evaluation for scale transformation. The paper shows there should be three main issues concerning the research on upscaling. The first one is that scale transformation based on discrete images from different sensors can be affected by the accuracy of normalization of imaging parameters. Another issue is about the lack of reasonable physical models for retrievals, resulting in the immaturity of continuous scaling based on them. Thirdly, some proposals try to model continuous scaling of retrievals using mathematical technique like fractal model, but it is still limited by the development of up-scaling technique. It is forecasted that: ① the first issue would be solved with the improvement of normalization of imaging parameters, contributing to better solution for specific application of remotely sensed data; ② meanwhile, modeling continuous scaling of retrievals would be an obvious trend. With deeper fusion of multidiscipline and development of quantitative remote sensing theory and technology, the other two issues would be solved well, which would be beneficial to revealing the true scaling rules of retrievals.