High-Resolution Sea Surface Temperature Retrieval from Landsat 8 OLI/TIRS Data at Coastal Regions

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.