Improved cloud detection in Along Track Scanning Radiometer (ATSR) data over the ocean

摘要

Valid estimates of sea surface temperature (SST) from satellite data [e.g., the Along Track Scanning Radiometer (ATSR)] are critically dependent upon the identification and removal of cloud from the data, but few cloud-screening algorithms for ATSR data have appeared in the literature. A new algorithm, the ATSR Split-and-Merge Clustering (ATSR/SMC) algorithm, for cloud masking ATSR data Is presented which evaluates every pixol in the image, is statistically reproducible, computationally efficient, and requires no knowledge of cloud type. Moreover it is effective in detecting multilayer cloud structures in a scene, which is a difficult task because such systems generally have bimodal statistical distributions, It also accurately detects glint radiance, which is quite common in at least one of the 1.6 mu m views, subpixel cloud contamination near-cloud boundaries and low-lying marine stratiform cloud. Historically, these issues have interfered with ATSR-based SST retrieval [see the work of Jones et al., (1996a,b) and the references cited therin]. The SSTs derived from. the cloud-free ocean pixels were validated with 96 buoy observations and the mean difference (buoy-SST) was +0.24 degrees C+/-0.51 degrees C. For the 103 pails of images (forwardadir views) tested, the mean 11 mu m BTs that result from SADIST (standard ATSR processing) cs. ATSR/SMC cloud detection are 0.4 degrees C (daytime) and 0.6 degrees C (nighttime) colder for SADIST than for ATSR/SMC, even though the SADIST cloud masks generally overdetect clouds relative to ATSR/SMC cloud masks. These results, plus others discussed in the test, support the conclusion that the new procedure produces cloud masks which are superior to the standard ATSR operational, cloud mask product and it retains substantially more valid pixels. The algorithm can be used in tropical and midlatitude regions. It is not designed to detect sea ice, and consequently should not be used in polar regions. Finally, the approach call easily be adapted to ATSR-2 data and to other darn to be taken from soon to be launched sensors. (C) Elsevier Science Inc., 1998. [References: 37]