An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions

Xiaolin Zhu, Eileen H. Helmer

Research output: Journal article publicationJournal articleAcademic researchpeer-review

83 Citations (Scopus)


Clouds and cloud shadows block land surface information in optical satellite images. Accurate detection of clouds and cloud shadows can help exclude these contaminated pixels in further applications. Existing cloud screening methods are challenged by cloudy regions where most of satellite images are contaminated by clouds. To solve this problem for landscapes where the typical frequency of cloud-free observations of a pixel is too small to use existing methods to mask clouds and shadows, this study presents a new Automatic Time-Series Analysis (ATSA) method to screen clouds and cloud shadows in multi-temporal optical images. ATSA has five main steps: (1) calculate cloud and shadow indices to highlight cloud and cloud shadow information; (2) obtain initial cloud mask by unsupervised classifiers; (3) refine initial cloud mask by analyzing time series of a cloud index; (4) predict the potential shadow mask using geometric relationships; and (5) refine the potential shadow mask by analyzing time series of a shadow index. Compared with existing methods, ATSA needs fewer predefined parameters, does not require a thermal infrared band, and is more suitable for areas with persistent clouds. The performance of ATSA was tested with Landsat-8 OLI images, Landsat-4 MSS images, and Sentinel-2 images in three sites. The results were compared with a popular method, Function of Mask (Fmask), which has been adopted by USGS to produce Landsat cloud masks. These tests show that ATSA and Fmask can get comparable cloud and shadow masks in some of the tested images. However, ATSA can consistently obtain high accuracy in all images, while Fmask has large omission or commission errors in some images. The quantitative accuracy was assessed using manual cloud masks of 15 images. The average cloud producer's accuracy of these 15 images is as high as 0.959 and the average shadow producer's accuracy reaches 0.901. Given that it can be applied to old satellite sensors and it is capable for cloudy regions, ATSA is a valuable supplement to the existing cloud screening methods.
Original languageEnglish
Pages (from-to)135-153
Number of pages19
JournalRemote Sensing of Environment
Publication statusPublished - 1 Sept 2018


  • Cloud detection
  • Cloud shadow
  • Mask
  • Optical satellite images
  • Time series

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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