Cloud-clearing techniques over land for land-surface temperature retrieval from the Advanced Along-Track Scanning Radiometer

Claire Bulgin, H. Sembhi, D. Ghent, J.j. Remedios, C.j. Merchant

Research output: Contribution to journalArticlepeer-review

Abstract

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).
Original languageEnglish
Pages (from-to)3594-3615
JournalInternational Journal of Remote Sensing
Volume35
Issue number10
DOIs
Publication statusPublished - 19 May 2014

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