Because of the reducing number of satellite images with various periods and good quality caused not only by changes in climate conditions, but also typhoons, rainy seasons, and various atmospheric changes, continuous image production and identification of changes in biophysical characteristics have become increasingly difficult. To remedy this, the production of simulated image that consider user-preferred periods and satellite geometric information can be one of alternatives. In this study, a simulated image generation method that uses the back project under collinearity conditions and atmospheric simulation is proposed. To create input data for the generation of simulated image, we produced SPOT-4 HRVIR satellite image that geometric and atmospheric corrections are processed. The analysis results on the similarity between simulated image and target image show average R-2 of 0.88. In addition, the experimental results for each image application field showed that (1) in the case of natural color composite, pixels having a correlation coefficient greater than 0.99 occupied 98% of the total image, (2) the results of the NDVI, an index frequently used in the forestry field, showed that the R-2 of two images was 0.91, and (3) the results of a similarity analysis showed that, after land cover classification was performed using maximum likelihood classification, an average similarity of 96% per land cover was achieved as compared with the target image. These results verify the similarity between the simulated image and target image, and the applicability of the simulated image.