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Multitemporal datasets can provide information on the different acquisition patterns of pixel values. The resultant generation of simulated images based on these patterns becomes practical and easy. In this paper, we propose a simple model for creating a simulated image based on a set of multitemporal satellite images and meteorological data utilizing a temporal correlation of multitemporal images. The satellite images and meteorological data used for the model can be easily accessed through open data sources. A dataset of 55 Landsat images was used to determine the relationship between the spectral radiance of the pixels and solar radiation, air temperature, humidity, rainfall, and visibility based on a multilinear regression analysis. This multilinear regression equation was then used to generate the simulated images obtained on the target dates. The results indicate that more than 91% of the pixels of the simulated images have correlation coefficients greater than 0.98 in all experimental cases. The correlation coefficients increased significantly when the normalized difference vegetation index (NDVI) and a reference image were applied to the man-made areas. The quality of the simulated images generated by this model primarily depends on changes in land cover. However, this effect can be reduced using NDVI interpolated from images obtained during the same season, or by employing a reference image acquired close to the date on which the simulated image is generated.
