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Bayesian Cloud Detection for Meris, Aatsr, and Their Combination : Volume 7, Issue 11 (06/11/2014)

By Hollstein, A.

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Book Id: WPLBN0004000054
Format Type: PDF Article :
File Size: Pages 41
Reproduction Date: 2015

Title: Bayesian Cloud Detection for Meris, Aatsr, and Their Combination : Volume 7, Issue 11 (06/11/2014)  
Author: Hollstein, A.
Volume: Vol. 7, Issue 11
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Henken, C. C., Preusker, R., Fischer, J., & Hollstein, A. (2014). Bayesian Cloud Detection for Meris, Aatsr, and Their Combination : Volume 7, Issue 11 (06/11/2014). Retrieved from http://hawaiilibrary.net/


Description
Description: Institute for Space Sciences, Department of Earth Sciences, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany. A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud masks were designed to be numerically efficient and suited for the processing of large amounts of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient amounts of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Summary
Bayesian cloud detection for MERIS, AATSR, and their combination

Excerpt
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, doi: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, doi: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, vol. 597, 13 pp., 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.; Gómez-Chova, L., Camps-Valls, G., Munoz-Marı, J., Calpe, J., and Moreno, J.: Cloud screening methodology for MERIS/AATSR Synergy products, in: Proc. 2nd MERIS/AATSR User Workshop, ESRIN, Frascati, 22–26, 2008.; Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, 2002.; Hanssen, A. and Kuipers, W.: On the Relationship Between the Frequency of Rain and Various Meteorological Parameters: with Reference to the Problem of Objective Forecasting, Staatsdrukkerij-en Uitgeverijbedrijf, 1965.; Heidinger, A. K., Evan, A. T., Foster, M. J., and Walther, A.: A naive Bayesian cloud-detection scheme derived from CALIPSO and applied within PATMOS-x, J. Appl. Meteorol. Clim., 51, 1129–1144, 2012.; Hollmann, R., Merchant, C., Saunders, R., Downy, C., Buchwitz, M., Cazenave, A., Chuvieco, E., Defourny, P., De Leeuw, G., Forsberg, R., Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., van Roozendael, M., and Wagner, W.: The ESA climate change initiative: satellite data records for essential climate variables, B. Am. Meteorol. Soc., 94, 1541–1552, 2013.; Kriegler, F., Malila, W., Nalepka, R., and Richardson, W.: Preprocessing transformations and their effects on multispectral recognition. Sixth International Symposium on Remotes Sensing of Environment, Ann Arbor, MI, 6, 97–131, 1969.; Llewellyn-Jones, D., Edwards, M., Mutlow, C., Birks, A., Barton, I., and Tait, H.: AATSR: Global-change and surface-temperature measurements from Envisat, ESA Bull.-Eur. Space, 105, 11–21, 2001.; Mackie, S., Embury, O., Old, C., Merchant, C., and Francis, P.: Generalized Bayesian cloud detection for satellite imagery, Part 1: Technique and validation for night-time imagery over land and sea, Int. J. Remote Sens., 31, 2573–2594, 2010a.; Mackie, S., Merchant, C., Embury, O., and Francis, P.: Generalized Bayesian cloud detection for satellite imagery, Part 2: Technique and validation for daytime imagery, Int. J. Remote Sens., 31, 2595–2621, 2010b.; Merchant, C., Harris, A., Maturi, E., and MacCallum, S.: Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval, Q. J. Roy. Meteor. Soc., 131, 2735–2755, 2005.; Miguel, A., Bruno, B., Jean-Loup, B., Mark, D., Florence, H., Ulf, K., Constantinos, M., Pierluigi, S., Bruno, G., and Jerome, B.: Sentinel-3 - the ocean and medium-resolution land mission for GMES operational services, ESA Bull.-Eur. Space, 131, 24–29, 2007.; Murtagh, F., Barreto, D., an

 

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