A Bayesian Cloud Mask for Sea Surface Temperature Retrieval

Bayesian methods are used to develop a cloud mask classification algorithm for use in an operational sea surface temperature (SST) retrieval processing system for Advanced Very High Resolution Radiometer (AVHRR) local area coverage (LAC) resolution data. Both radiative and spatial features are incorporated in the resulting discriminant functions, which are determined from a large training sample of cloudy and clear observations. This approach obviates the need to specify the arbitrary thresholds used by hierarchical cloud-clearing methods, provides an estimate of the probability that an instantaneous field of view is cloudy (clear), and allows the skill of different cloud discriminant models to be objectively analyzed. Results show that spatial information is of particular importance in reducing the false alarm rate of the cloudy class. However, while the use of complex textural measures such as gray-level difference statistics—as opposed to simple statistics such as the standard deviation—improves the skill of nighttime cloud-masking algorithms, they are of little advantage during daytime hours. Cloud mask discriminant models having similar high Kuipers’ performance index scores (i.e., 0.935) are developed for both day and night satellite data from the Southern Hemisphere midlatitudes. Applied to LAC orbital (i.e., operational) data, the characteristics of the cloud masks appear to be similar to those derived from analysis of the training sample data. However, in this case, to enhance processing performance, a hybrid algorithm is employed—obviously cloudy instantaneous fields of view (IFOVs) are first removed via a gross threshold check and the Bayesian method applied only to the remaining IFOVs. This same (hybrid) algorithm is also applied to an ensemble of 30 days of AVHRR LAC data from the New Zealand region. Analysis of the resulting time-composited SST data (means and standard deviations) shows there is little evidence of a day‐night bias in the performance of the Bayesian cloud-masking algorithm and that the resulting SST data may be used to determine the variability of oceanographic features. Although this paper uses AVHRR data to demonstrate the principles of the Bayesian cloud-masking algorithm, there is no reason why the approach could not be used with other instruments.

[1]  F. Ostapoff,et al.  The Intradiurnal Temperature Variation in the Upper Ocean Layer , 1974 .

[2]  Ronald M. Welch,et al.  An intercomparison of artificial intelligence approaches for polar scene identification , 1993 .

[3]  Robert P. d'Entremont.  Low- and Midlevel Cloud Analysis Using Nighttime Multispectral Imagery , 1986 .

[4]  W. Emery,et al.  A comprehensive comparison between satellite-measured skin and multichannel sea surface temperature , 1992 .

[5]  C. Long,et al.  Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data , 1991 .

[6]  G. Hunt.  Radiative properties of terrestrial clouds at visible and infra-red thermal window wavelengths , 1973 .

[7]  C. Hepplewhite,et al.  Remote observation of the sea surface and atmosphere The oceanic skin effect , 1989 .

[8]  A. H. Murphy,et al.  Probability, Statistics, And Decision Making In The Atmospheric Sciences , 1985 .

[9]  R. Saunders,et al.  An improved method for detecting clear sky and cloudy radiances from AVHRR data , 1988 .

[10]  P. Minnett.  Consequences of sea surface temperature variability on the validation and applications of satellite measurements , 1991 .

[11]  Robert A. Schowengerdt,et al.  Techniques for image processing and classification in remote sensing , 1983 .

[12]  Thomas M. Smith,et al.  Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation , 1994 .

[13]  James J. Simpson,et al.  An automated cloud screening algorithm for daytime advanced very high resolution radiometer imagery , 1990 .

[14]  L. McMillin,et al.  Theory and validation of the multiple window sea surface temperature technique , 1984 .

[15]  H. Bryden,et al.  Energetic eddies at the Northern edge of the Antarctic circumpolar current in the Southwest Pacific , 1985 .

[16]  E. Paul McClain,et al.  Global sea surface temperatures and cloud clearing for aerosol optical depth estimates , 1989 .

[17]  Jason I. Gobat,et al.  Improved cloud detection in GOES scenes over the oceans , 1995 .

[18]  William G. Pichel,et al.  Comparative performance of AVHRR‐based multichannel sea surface temperatures , 1985 .

[19]  J. Delderfield,et al.  The Along Track Scanning Radiometer (ATSR) for ERS1 , 1986, Other Conferences.

[20]  Michael Uddstrom,et al.  Satellite Cloud Classification and Rain-Rate Estimation Using Multispectral Radiances and Measures of Spatial Texture , 1996 .

[21]  I. J. Barton,et al.  Validation of the ATSR in Australian Waters , 1995 .

[22]  P. J. Minnett,et al.  Observations of sea-surface temperature for climate research , 1983, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[23]  G. Gutman,et al.  Satellite daytime image classification for global studies of Earth's surface parameters from polar orbiters , 1992 .

[24]  S. K. Sengupta,et al.  Marine Stratocumulus Cloud Fields off the Coast of Southern California Observed Using LANDSAT Imagery. Part II: Textural Analysis , 1988 .

[25]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  William L. Smith,et al.  Observations of the infrared radiative properties of the ocean-implications for the measurement of sea surface temperature via satellite remote sensing , 1996 .

[27]  C. Walton,et al.  Nonlinear Multichannel Algorithms for Estimating Sea Surface Temperature with AVHRR Satellite Data , 1988 .

[28]  Toshiro Inoue,et al.  On the Temperature and Effective Emissivity Determination of Semi-Transparent Cirrus Clouds by Bi-Spectral Measurements in the 10μm Window Region , 1985 .

[29]  R. A. Heath.  A review of the physical oceanography of the seas around New Zealand ― 1982 , 1985 .

[30]  Joann Parikh,et al.  A comparative study of cloud classification techniques , 1977 .

[31]  Kwo-Sen Kuo,et al.  Segmentation of polar scenes using multi-spectral texture measures and morphological filtering , 1994 .

[32]  Mark A. Saunders,et al.  Reducing Cloud Contamination in ATSR Averaged Sea Surface Temperature Data , 1996 .

[33]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[34]  Sonia C. Gallegos,et al.  A new automated method of cloud masking for advanced very high resolution radiometer full-resolution data over the ocean , 1993 .

[35]  James J. Simpson,et al.  Automated cloud screening of AVHRR imagery using split-and-merge clustering☆ , 1991 .