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.

Validation of Satellite Sea Surface Temperatures and Long-Term Trends in Korean Coastal Regions over Past Decades (1982–2018)

“…The dominant factors in such different ratios are attributed to diverse changes in local air-sea conditions from complicated bathymetry, tidal currents and mixing, turbidity, sea fog, and clouds. Another factor influencing near-shore satellite-derived SSTs could be submesoscale SST variability, as observed in the Landsat SST images [26]. The findings of this study could be helpful for understanding the warming or cooling rates in coastal regions with various characteristics of local seas.…”

“…Moreover, it should be noted that strong SST gradients in the coastal zone are not resolved in lower-resolution OISST images [26]. In the southern near-coastal regions, many island-induced wake variabilities can also yield warm or cold bias in the satellite SSTs relative to the in situ measurements.…”

“…The RMSE of AVHRR SSTs at the coastline ranged from 1.0 K to 2.0 K depending on the temporal difference of the matchup data in the coastal waters of the UK and Ireland [24]. In Korean coastal regions, global SST products showed relatively large RMSE values of over 1.27 K and warm bias over 0.31 K [25], and high-resolution Landsat-8 SSTs had relatively small RMSE values from 0.59 to 0.72 K, as compared with coastal buoy temperatures [26].…”

Land–Sea Interaction: Integrating Climate Adaptation Planning and Maritime Spatial Planning in the North Adriatic Basin

“…The interpretation of these layers' integration within the same vector-unit allows us to evaluate the overall sea-based context and its spatial characteristics, connecting the Sea and Maritime Approach with the Land and Urban Approach. The scientific literature on SST recognizes the SWA algorithm as the most accurate to support water body thermal analysis [57][58][59][60].…”

Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea

“…The use of TIRS band 11 is no longer limited since stray light correction was implemented and uncertainties were reduced starting with Landsat Collection 1, distributed by the USGS since 2017 [35]. The authors of [36] have used the data from this collection and in situ observations to determine the splitwindow algorithm coefficients and to evaluate its accuracy based on a sufficiently large number of comparisons with in situ data from coastal waters around the Korean Peninsula. Other attempts were only based on a small number of Landsat 8 matchup points and/or applied to a very narrow range of temperatures and atmospheric conditions [27,29,32,37].…”