We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along-Track Scanning Radiometer (AATSR) instrument and compare these to the standard threshold-based SADIST cloud detection scheme. We use a manually
classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land-surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8–9% over SADIST (maximum
score of 77.93% compared with 69.27%). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3 × 3 pixel domain. We find an increase of 5–7% in the observations
falling within this tolerance when using Bayesian methods (maximum of 92.02% compared with 85.69%). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to be provided by the Sea and Land
Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
School of Geosciences, University of Edinburgh, Edinburgh, UK
Space Research Centre, University of Leicester, Leicester, UK
Department of Meteorology, University of Reading, Reading, UK
Publication date: May 19, 2014
More about this publication?