Articles | Volume 8, issue 4
https://doi.org/10.5194/amt-8-1757-2015
https://doi.org/10.5194/amt-8-1757-2015
Research article
 | 
15 Apr 2015
Research article |  | 15 Apr 2015

Bayesian cloud detection for MERIS, AATSR, and their combination

A. Hollstein, J. Fischer, C. Carbajal Henken, and R. Preusker

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Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Cited articles

Carbajal Henken, C. K., Lindstrot, R., Preusker, R., and Fischer, J.: FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations, Atmos. Meas. Tech. Discuss., 7, 4909–4947, https://doi.org/10.5194/amtd-7-4909-2014, 2014.
Coppo, P., Ricciarelli, B., Brandani, F., Delderfield, J., Ferlet, M., Mutlow, C., Munro, G., Nightingale, T., Smith, D., Bianchi, S., Nicol, P., Kirschstein, S., Hennig, T., Engel, W., Frerick, J., and Nieke, J.: SLSTR: a high accuracy dual scan temperature radiometer for sea and land surface monitoring from space, J. Mod. Optic., 57, 1815–1830, https://doi.org/10.1080/09500340.2010.503010, 2010.
English, S., Eyre, J., and Smith, J.: A cloud-detection scheme for use with satellite sounding radiances in the context of data assimilation for numerical weather prediction, Q. J. Roy. Meteor. Soc., 125, 2359–2378, 1999.
Fomferra, N. and Brockmann, C.: Beam-the ENVISAT MERIS and AATSR toolbox, in: MERIS (A)ATSR Workshop 2005, 597, p. 13, 2005.
Gómez-Chova, L., Camps-Valls, G., Amorós-López, J., Guanter, L., Alonso, L., Calpe, J., and Moreno, J.: New cloud detection algorithm for multispectral and hyperspectral images: Application to ENVISAT/MERIS and PROBA/CHRIS sensors, in: IEEE International Geoscience and Remote Sensing Symposium, IGARSS, 2757–2760, 2006.
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Short summary
Cloud detection is one of the key components for the exploitation of Earth observation images. We discuss the use of probabilistic algorithms for MERIS and AATSR on-board the ENVISAT satellite. As a new approach, we used an automated search to find the best combination of channels for the algorithm, which led to a number of unusual combinations that have not been used in the past. We show how very small samples of manually classified cloud truth images can be used to set up efficient algorithms.