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TitleTesting Near Real-Time Detection of Contaminated Pixels in AVHRR Composites
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorCihlar, J; Latifovic, R; Chen, J M; Li, Z
SourceCanadian Journal of Remote Sensing vol. 25, issue 2, 1999 p. 160-170,
Alt SeriesEarth Sciences Sector, Contribution Series 20042579
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf
Subjectsremote sensing; CECANT
Illustrationsgraphs; satellite imagery
Released2014 07 31
AbstractWhen using composite optical satellite images for land studies, an accurate and sensitive method is needed to detect pixels contaminated by unwanted atmospheric and surface effects. In this paper, we have examined the feasibility of using an algorithm previously developed for post-season analysis but in a 'forward' mode, i.e for identifying fully or partly contaminated pixels in current season data. The CECANT algorithm (Cloud Elimination from Composites using Albedo and NDVI Trend; Cihlar, 1996) uses AVHRR channel 1 reflectance to detect strongly contaminated pixels (bright clouds, snow), and the normalized difference vegetation index (NDVI) to identify partially contaminated pixels over land. However, this approach needs a complete NDVI seasonal trajectory to derive the adaptive thresholds. Since some important applications need near-real time processing we have examined the possibility of deriving the thresholds from previous (historical) AVHRR data. Using four years of AVHRR data of Canada we found the accuracy to vary (55-100%), with thresholds from a single year yielding anomalous results in some compositing periods. On the other hand, the use of thresholds derived from averaged data (over 3 years in this case) produced more accurate and consistent results. Assuming that contamination masks derived from each year's data are 100% accurate, the masks based on the three years of AVHRR data were 75-95% correct (4-year average), depending on the compositing period. By relaxing one of the CECANT thresholds the errors of omission and commission became more equally balanced (~10 and 20%, respectively). The performance of an alternative, simplified cloud screening method (based on fixed reflectance and temperature thresholds) was also examined for a comparison, and was found to have lower accuracy (58% compared to 87%, 4-year average). It is concluded that CECANT may be effectively applied in near-real time processing of satellite optical data, provided that the data used to derive the thresholds represent those to be corrected in terms of satellite measurement and scene characteristics. Since this condition is not likely to be fully or reliably met, a reprocessing at the end of the growing season will be desirable for applications requiring high radiometric accuracy.

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