A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA-AVHRR images

E.A. Addink, A. Stein

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)

Abstract

The potential of using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images for large areas is often limited by cloud cover. It could be increased when small clouds are replaced by estimated reflection and emission values. In this study seven replacement methods are compared, ranging from simple replacement to stratified co-kriging. Images of subsequent days serve as co-variable, enabling the use of spatial and temporal information. For validation, cloud-free pixels were replaced with four patterns of artificially clouded pixels. Co-kriging as a combination of both temporal and spatial information resulted in the best estimates, reducing the mean squared errors by 20-70%. Stratification of the image did not result in better cloud replacement. Once kriging options have been implemented in existing image processing packages, co-kriging will be an easy-to-use solution to missing values, provided that images of subsequent days of low cloud coverage are available. | The potential of using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images for large areas is often limited by cloud cover. It could be increased when small clouds are replaced by estimated reflection and emission values. In this study seven replacement methods are compared, ranging from simple replacement to stratified co-kriging. Images of subsequent days serve as co-variable, enabling the use of spatial and temporal information. For validation, cloud-free pixels were replaced with four patterns of artificially clouded pixels. Co-kriging as a combination of both temporal and spatial information resulted in the best estimates, reducing the mean squared errors by 20-70%. Stratification of the image did not result in better cloud replacement. Once kriging options have been implemented in existing image processing packages, co-kriging will be an easy-to-use solution to missing values, provided that images of subsequent days of low cloud coverage are available.
Original languageEnglish
Pages (from-to)961-977
JournalInternational Journal of Remote Sensing
Volume20
Issue number5
DOIs
Publication statusPublished - 1999

Keywords

  • remote sensing
  • image processing
  • land use
  • geostatistics

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