Generalised Bayesian cloud detection for satellite imagery. part 2: technique and validation for day-time imagery

Mackie, S., Merchant, C. J. ORCID: https://orcid.org/0000-0003-4687-9850, Embury, O. ORCID: https://orcid.org/0000-0002-1661-7828 and Francis, P. (2010) Generalised Bayesian cloud detection for satellite imagery. part 2: technique and validation for day-time imagery. International Journal of Remote Sensing, 31 (10). pp. 2595-2621. ISSN 0143-1161 doi: 10.1080/01431160903051711

Abstract/Summary

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.