Journal of Oceanography

, Volume 72, Issue 5, pp 707–726 | Cite as

Reconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea

  • Hong-Ngu T. HuynhEmail author
  • Aida Alvera-Azcárate
  • Alexander Barth
  • Jean-Marie Beckers
Original Article


Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. Unfortunately, the SST data sources in the South China Sea (SCS) are not abundant due to sparse measurements of in situ SST and a high percentage of missing data in the satellite-derived SST. Therefore, SST data sets with low resolution and/or a short-term period have often been used in previous researches. Here we used Data INterpolating Empirical Orthogonal Functions, a self-consistent and parameter-free method for filling in missing data, to reconstruct the daily nighttime 4-km AVHRR Pathfinder SST for the long-term period spanning from 1989 to 2009. In addition to the reconstructed field, we also estimated the local error map for each reconstructed image. Comparisons between the reconstructed and other data sets (satellite-derived microwave and in situ SSTs) show that the results are reliable for use in many different researches, such as validating numerical models, or identifying and tracking meso-scale oceanic features. Moreover, the Empirical Orthogonal Function (EOF) analysis of the reconstructed SST and the reconstructed SST anomalies clearly shows the subseasonal, seasonal, and interannual variability of SST under the influence of monsoon and El Niño-Southern Oscillation (ENSO), as well as reveals some oceanic features that could not be captured well in previous EOF analyses. The SCS SST often lags ENSO by about half a year. However, in this study, we see that the time lag changes with the frequencies of the SST variability, from 1 to 6 months.


South China Sea AVHRR Pathfinder SST Subseasonal Seasonal and interannual variability Monsoon  ENSO DINEOF 



Daily nighttime 4-km AVHRR Pathfinder SST data used in this study were obtained from the Physical Oceanography Distributed Active Archive Center (PODAAC) at the NASA Jet Propulsion Laboratory ( TMI data produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project are available at In situ data were downloaded from NOAA National Oceanographic Data Center (NODC), World Ocean Database 2009 (WOD09) ( Wind data were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ( The Niño 3 SST was downloaded from Calculations were run on the super-computer NIC3 of the University of Liège, and HMEM of the Université catholique de Louvain (CISM/UCL) and the Consortium des Équipements de Calcul Intensif en Fédération Wallonie Bruxelles (CÉCI) funded by the Fond de la Recherche Scientifique de Belgique (FRS-FNRS). We are grateful to the anonymous reviewers for valuable comments on the manuscript. Suggestions by Prof. Joji Ishizaka are appreciated. This study was carried out within the context of the BESST (SR/12/158) project funded by the Belgian Science Policy (BELSPO) within the framework of the Research Program for Earth Observation STEREO II. The Vietnam Ministry of Education and Training is gratefully acknowledged for funding H.-N.T. Huynh’s Ph.D. scholarship.


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Copyright information

© The Oceanographic Society of Japan and Springer Japan 2016

Authors and Affiliations

  • Hong-Ngu T. Huynh
    • 1
    Email author
  • Aida Alvera-Azcárate
    • 1
  • Alexander Barth
    • 1
  • Jean-Marie Beckers
    • 1
  1. 1.GeoHydrodynamics and Environment Research, AGOUniversity of LiègeLiègeBelgium

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