Thick cloud removal in Landsat images based on autoregression of Landsat time-series data

Ruyin Cao, Yang Chen, Jin Chen, Xiaolin Zhu, Miaogen Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

41 Citations (Scopus)


Thick-cloud contamination causes serious missing data in Landsat images, which substantially limits applications of these images. To remove thick clouds from Landsat data, the most popular methods employ auxiliary data such as a cloud-free image of the same area acquired on another date (referred to as the “reference image”). However, the performance of most previous methods strongly depends on the usefulness of the specific reference image, but in some cases high-quality cloud-free reference images are rarely available. In addition, some of these methods ignore the use of partially cloud-contaminated reference images, but clear pixels in these images can be very useful. To address these issues, a new cloud-removal method (AutoRegression to Remove Clouds (ARRC)) has been developed in this study. The most important improvement of ARRC is that it considers autocorrelation of Landsat time-series data and employs multi-year Landsat images including partially cloud-contaminated images in the cloud-removing process. ARRC also addresses the cases in which autocorrelation of Landsat time series might be adversely affected by abrupt land cover changes over multiple years. We compared ARRC with the widely-used MNSPI (modified neighborhood similar pixel interpolator) method at four challenging sites, including an urban area in Beijing and three croplands, in the North China Plain, northeastern Vietnam, and Iowa, USA. Results from the cloud-simulated images showed that ARRC performed better than MNSPI and achieved lower RMSE values (e.g., 0.02129 vs. 0.03005, 0.03293 vs. 0.04725, 0.02740 vs. 0.03556, and 0.03303 vs. 0.03973 in the near-infrared band at the four testing sites, respectively). Moreover, the experiments demonstrated improved performance when clear pixels in partially cloud-contaminated images were used by ARRC. Furthermore, cloud-free images reconstructed by ARRC were visually better than those reconstructed by MNSPI when both approaches were applied to actual cloud-contaminated Landsat images.

Original languageEnglish
Article number112001
JournalRemote Sensing of Environment
Publication statusPublished - Nov 2020


  • Cloud removal
  • Gap filling
  • Landsat images
  • Partially cloud-contaminated images
  • Thick clouds

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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