The increasing availability of satellite information has improved Earth observation applications globally. However, primary satellite information is not as immediate as desirable. Indeed, besides the geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical imagery. Actually, such a contamination is intended as missing information and should be replaced. However, because the most common cloud masking algorithms take advantage by employing thermal images, here the objective is to provide an alternative algorithm suitable for multispectral imagery only. In addition, the work combines a multispectral/multitemporal approach as an effective method to retrieve daytime cloudless and shadow-free optical imagery. Experiment is undertaken upon mid- to low-spatial resolution data from Landsat 5 TM and Landsat 8 OLI, each for a different scene. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over 1 year. The approach relies on a clouds and cloud shadows masking step, based on spectral features, a band-by-band multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase based on automatic selection of the most suitable pixels from the stack. Results have been compared with a recognized masking algorithm approach and tested with uncontaminated image samples for the same scene. Accuracy and spectral features of the results provide high consistency.