  Cloud removal is an essential task in remote sensing data analysis. As the
image sensors are distant from the earth ground, it is likely that part of the
area of interests is covered by cloud. Moreover, the atmosphere in between
creates a constant haze layer upon the acquired images. To recover the ground
image, we propose to use scattering model for temporal sequence of images of
any scene in the framework of low rank and sparse models. We further develop
its variant, which is much faster and yet more accurate. To measure the
performance of different methods {\em objectively}, we develop a semi-realistic
simulation method to produce cloud cover so that various methods can be
quantitatively analysed, which enables detailed study of many aspects of cloud
removal algorithms, including verifying the effectiveness of proposed models in
comparison with the state-of-the-arts, including deep learning models, and
addressing the long standing problem of the determination of regularisation
parameters. The latter is companioned with theoretic analysis on the range of
the sparsity regularisation parameter and verified numerically.
