• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.663-675
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• 2011

Sea Surface Temperatures (SSTs) using the equations of NOAA (National Oceanic and Atmospheric Administration) / NESDIS (National Environmental Satellite, Data, and Information Service) were validated over the seas around Korea with satellite-tracked drifter data. A total 1,070 of matchups between satellite data and drifter data were acquired for the period of 2009. The mean rms errors of Multi- Channel SSTs (MCSSTs) and Non-Linear SSTs (NLSSTs) were evaluated to, in most of the cases, less than $1^{\circ}C$. However, the errors revealed dependencies on atmospheric and oceanic conditions. For the most part, SSTs were underestimated in winter and spring, whereas overestimated in summer. In addition to the seasonal characteristics, the errors also presented the effect of atmospheric moist that satellite SSTs were estimated considerably low ($-1.8^{\circ}C$) under extremely dry condition ($T_{11{\mu}m}-T_{12{\mu}m}$ < $0.3^{\circ}C$), whereas the tendency was reversed under moist condition. Wind forcings induced that SSTs tended to be higher for daytime data than in-situ measurements but lower for nighttime data, particularly in the range of low wind speeds. These characteristics imply that the validation of satellite SSTs should be continuously conducted for diverse regional applications.