The transmission of satellite data is adversely affected by cloud cover; therefore, precise cloud detection plays an important role in recognizing remote sensing image targets and quantitatively inverting parameters. This study addresses the challenges of accurately identifying bright surfaces, thin clouds, broken clouds, and cloud boundaries and the stability of cloud detection accuracy across different scale features. We calculate linear regression on short-term time series datasets, using the slope-change trend of apparent reflectance of front and back time series datasets as the input. To fully leverage information from different scales, we employ the UNet++ model for cloud detection, which boasts a unique dense skip structure and deep supervision structure. Compared with U-Net, SegNet, and UNet++ of the single-temporal dataset, our proposed method can effectively highlight multiscale features and increase the sensitivity for bright surfaces, cloud-boundary contour, and thin-cloud information. Our results demonstrate that the proposed method achieves a high accuracy of 98.21% in cloud detection, and the false detection and missing detection rates are reduced to 1.07% and 3.12%, respectively. Furthermore, our method effectively reduces the interference of bright surfaces on cloud identification, such as barren lands, roads, buildings, ice, and snow, while improving thin-cloud identification accuracy. Therefore, our proposed method is suitable for remote sensing images of different underlying surfaces.