This study aims to balance the contradiction caused by large satellite cloud image data and the limitations of transmission channels and storage space. We proposed a compressed sensing approach with highly reconstructive quality that utilizes temporal-spatial correlation of adjacent sequence cloud images based on Tetrolet transform. The approach introduces Tetrolet transform into the sparse representation step of compressed sensing for its remarkable representation of important image information

such as directional texture and edge

which can depict the detail and texture structure of satellite cloud images well. Meanwhile

considering the correlation between sequences of the satellite images

three adjacent cloud images are bound to form a Group Of Pictures（ GOP）

in which the intermediate time cloud image is the reference picture

and the difference with its adjacent cloud images is calculated to form the difference images. Finally

GOP is reconstructed by the projected Landweber algorithm with smoothing operation under the compressed sensing framework. Experimental results show that Tetrolet transform is appropriate for the sparse representation of satellite cloud images. The use of the temporal-spatial correlation of GOP can improve the visual quality and quantitative measurements of reconstructed cloud images. When the sampling rate for cloud images of infrared one

water vapor

and visible light channel is lower than 0. 2

the Peak Signal-to-Noise Ratio（ PSNR） of the reconstructed images is improved by an average of 7. 48 dB

13. 51 dB and 6. 15 dB

respectively

compared with that obtained by traditional methods. High-quality cloud image can be reconstructed by obtaining random measurements using the p roposed method. This method provides a feasible solution for the low-bit-rate compression of cloud images and is significant for the compressed sampling of other sequence images.