Cloud cover and its shadow are a common and unavoidable issue in various optical satellite images that cause limitations in observing Earth’s objects. In other word, they are noise for observing ground objects. Therefore, providing precise and suitable algorithms for detecting cloud and its shadow in various types of satellite optical images is an important task. Landsat 8 sensors provide free and easy accessibility, suitable resolutions, band variation and archived images to users in order to do various applications. Detecting cloud and its shadow in Landsat 8 images is an important mission before any other processing, since ground targets cannot be observed in cloudy days. Here we suggest a decision tree method based on thresholding approach and using two new bands of Landsat 8 i.e., Band 1 and Band 9 to obtain cloud and its shadow from Landsat 8 images. In the proposed method, at first, digital numbers are converted into reflectance values. Then cloud and its shadow are detected using (band 1 and band 9) and (band 1, band 5, band 6, and band 9), respectively. Afterwards, morphologic operators are employed to improve the quality of the cloud and its shadow mask. Finally, accuracy assessment is done. The proposed approach was applied to five images over three regions with low to high cloud cover percentage. Based on the obtained results, our simple proposed approach can detect cloud and its shadow with an overall accuracy higher than 98%. According to accuracy assessment results, pixel-qa product of Landsat 8 is not proper to extract cloud and its shadow, since it has errors. Moreover, the accuracy of the proposed method was compared to Kang et al., cloud detection approaches. Based on outcomes, our proposed method outperforms Kang et al., cloud detection approaches. In order to improve the robustness of the proposed method, it is recommended to apply it over other case studies and data sets.