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Abstract: Improved oceanic cloud climatologies and more accurate sea surface temperature (SST) from satellite data (e.g., the Along Track Scanning Radiometer-2 (ATSR-2)) depend upon the identification, and in the case of SST, masking of cloud from the data. Few cloud-screening algorithms for ATSR-2 data, however, have been published. This is unfortunate because, unlike the original ATSR (hereafter referred to as ATSR-1), ATSR-2 has three bands of visible data in addition to its traditional suite of near-to-far thermal infrared bands. A new neural network-based cloud detection algorithm, which accommodates both the nadir and forward views of the ATSR-2, is presented. It evaluates every pixel in the scene, is statistically reproducible, computationally efficient, and requires no knowledge of cloud type. Moreover, the algorithm accurately detects glint radiance, which is often seen in at least one of the 1.6 μm (and/or visible) views, subpixel cloud near cloud boundaries, low-lying marine stratiform cloud, and does not misinterpret actual ocean thermal gradients as cloud. These latter issues have interfered with many SST retrievals in the past. Dual view/dual channel SST retrievals were derived and validated against buoy data (1 m depth). RMS error between retrieved SST and the buoy data is typically about 0.3 K in regions of minimum SST gradient. Pre-processing of the data is done prior to analysis because some artifacts not seen in ATSR-1 data can seriously compromise both cloud detection and SST retrieval. Detailed comparison between the ATSR operational cloud detection scheme and that developed herein, based upon 1077 scenes, shows that the operational product consistently misinterprets regions of cold but cloud-free oceanic thermal gradient as cloud, leading to a sampling-related warm bias in many of the operational SST products. The basis for this misinterpretation has been identified and quantified. Thus, the operational cloud detection scheme may have to be revised if a fully representative global ocean SST dataset is to be obtained from ATSR data. [Copyright &y& Elsevier]

Presents a study which proposed a hybrid method for automatic clustering of satellite-observed natural scenes. Complex task of classification of natural features in multivariate satellite data; Improved Split-And-Merge Clustering algorithm; ISODATA clustering algorithm; Improved image clustering of natural scenes; Results and discussion.

Focuses on a study which presented a multispectral algorithm used in cloud shadow detection and the removal in daytime AVHRR scenes over land. Importance of the accurate detection of cloud shadows in AVHRR scenes; Methodology used to conduct the study; Results of the study.