Abstract
High-resolution sea surface temperature (SST) images are essential to study the highly variable small-scale oceanic phenomena in a coastal region. Most previous SST algorithms are focused on the low or medium resolution SST from the near polar orbiting or geostationary satellites. The Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) makes it possible to obtain high-resolution SST images of coastal regions. This study performed a matchup procedure between 276 Landsat 8 images and in-situ temperature measurements of buoys off the coast of the Korean Peninsula from April 2013 to August 2017. Using the matchup database, we investigated SST errors for each formulation of the Multi-Channel SST (MCSST) and the Non-Linear SST (NLSST) by considering the satellite zenith angle (SZA) and the first-guess SST. The retrieved SST equations showed a root-mean-square error (RMSE) from 0.59 to 0.72 °C. The smallest errors were found for the NLSST equation that considers the SZA and uses the first-guess SST, compared with the MCSST equations. The SST errors showed characteristic dependences on the atmospheric water vapor, the SZA, and the wind speed. In spite of the narrow swath width of the Landsat 8, the effect of the SZA on the errors was estimated to be significant and considerable for all the formations. Although the coefficients were calculated in the coastal regions around the Korean Peninsula, these coefficients are expected to be feasible for SST retrieval applied to any other parts of the global ocean. This study also addressed the need for high-resolution coastal SST, by emphasizing the usefulness of the high-resolution Landsat 8 OLI/TIRS data for monitoring the small-scale oceanic phenomena in coastal regions.
Highlights
Sea surface temperature (SST) plays an important role in monitoring and understanding a variety of oceanic and atmospheric phenomena in space and time
Cloud mask algorithms applicable to the Landsat 8 OLI/TIRS data have been developed by various studies, including the Automated Cloud Cover Assessment (ACCA), the Web Enabled Landsat Data (WELD) Cloud Cover Assessment (CCA) algorithm, the Function of Mask (Fmask) algorithm, and so on [63,64,65]
Cloud mask algorithms applicable to the Landsat 8 OLI/TIRS data have been developed by various studies, including the Automated Cloud Cover Assessment (ACCA), the Web Enabled Landsat Data (WELD) Cloud Cover Assessment (CCA) algorithm, the Function of Mask R(FemmotaesSken) sa. 2lg01o9r,i1t1h,m26,87and so on [63,64,65]
Summary
Sea surface temperature (SST) plays an important role in monitoring and understanding a variety of oceanic and atmospheric phenomena in space and time. The satellite-observed SST databases have been produced and extensively utilized for investigating SST fronts [3,4], mesoscale eddy dynamics [5], ocean surface current [6], air-sea interaction [7], and their effects on climate change [8]. They are used as input data for atmospheric and oceanic circulation models as well as for data assimilation purposes [9,10]. In the onshore or offshore regions near the coasts, satellite SST images with a finer spatial resolution, equal to or below 100 m, are required because of the highly complicated coastal lines and complex coastal morphology, the presence of numerous islands, estuaries, small-scale SST gradients that affect fisheries and air-sea fluxes, as well as other oceanic features in coastal regions
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