Retrievals of Sea Surface Current Vectors from Geostationary Satellite Data (Himawari-8/AHI)

  • Hee-Young Kim
  • Kyung-Ae ParkEmail author
  • Hee-Ae Kim
  • Sung-Rae Chung
  • Seong-Hoon Cheong
Original Article


An operational sea surface current (SSC) retrieval algorithm was developed using consecutive Himawari-8/AHI data based on a feature tracking method. Comparative analyses were conducted to determine the appropriate input data for the SSC retrieval algorithm. Investigation of the input data revealed some limitations in the use of single-band brightness temperatures caused by atmospheric features under moist conditions, especially in the mid- and low-latitude regions. Because of the motion of atmospheric features, cloud and cloud-contaminated pixels tended to contribute to the overestimation of SSC. To reduce overestimation, sea surface temperature images were used as input data and the feature tracking method was applied to calculate the displacement of the surface current vectors. The estimated currents were subjected to a quality control process to remove erroneous vectors. The accuracy of the retrieved surface currents was assessed by comparing the results with the quality-controlled currents obtained from surface drifters in the full-disk region of Himawari-8/AHI. The results revealed that the estimated current speeds and directions agreed with the drifter-based calculated values—the root-mean-square (bias) errors were 0.35 ms−1 (0.11 ms−1) and 33.28° (5.47°), respectively. The estimated current field showed diverse dynamic ocean features, such as a rotating feature around a mesoscale eddy and the characteristic meandering pattern of the Kuroshio Current. Hourly varying surface current fields from geostationary satellite data with high spatio-temporal resolution are expected to augment oceanic and atmospheric applications in real time.


Sea surface current Himawari-8/AHI Sea surface temperature Feature tracking 



This work was supported by “Development of Scene and Surface Analysis Algorithms” project, funded by ETRI, which is a subproject of “Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2019-01)” program funded by NMSC (National Meteorological Satellite Center) of KMA (Korea Meteorological Administration); and the Korea Meteorological Administration Research and Development Program under Grant KMI2018-05110.


  1. Barton, I.J.: Ocean currents from successive satellite images: the reciprocal filtering technique. J. Atmos. Ocean. Technol. 19(10), 1644–1689 (2002)CrossRefGoogle Scholar
  2. Bessho, K., Date, M., Hayashi, A., Ikeda, T., Imai, H., Inoue, Y., Kumagai, T., Miyakawa, H., Murata, T., Ohno, A., Okuyama, R., Oyama, Y., Sasaki, Y., Shimazu, K., Shimoji, Y., Sumida, M., Suzuki, H., Taniguchi, H., Tsuchiyama, D., Uesawa, H., Yokota, Yoshida, R.: An introduction to Himawari-8/9—Japan’s newgeneration geostationary meteorological satellites. J. Meteorol. Soc. Jpn. 94, 151–183 (2016)CrossRefGoogle Scholar
  3. Bigg, G.R., Jickells, T.D., Liss, P.S., Osborn, T.J.: The role of the oceans in climate. International Journal of Climatology: A journal of the Royal Meteorological Society. 23(10), 1127–1159 (2003)CrossRefGoogle Scholar
  4. Bowen, M.M., Emery, W.J., Wilkin, J.L., Tildesley, P.C., Barton, I.J., Knewtson, R.: Extracting multiyear surface currents from sequential thermal imagery using the maximum cross-correlation technique. J. Atmos. Ocean. Technol. 19, 1665–1676 (2002)CrossRefGoogle Scholar
  5. Chen, Y., Han, Y., van Delst, P., Weng, F.: Assessment of shortwave infrared sea surface reflection and nonlocal thermodynamic equilibrium effects in the community radiative transfer model using IASI data. J. Atmos. Ocean. Technol. 30(9), 2152–2160 (2013)CrossRefGoogle Scholar
  6. Chubb, S.R., Mied, R.P., Shen, C.Y., Chen, W., Evans, T.E., Kohut, J.: Ocean surface currents from AVHRR imagery: comparison with land-based HF radar measurements. IEEE Trans. Geosci. Remote Sens. 46(11), 3647–3660 (2008)CrossRefGoogle Scholar
  7. Crocker, R.I., Matthews, D.K., Emery, W.J., Baldwin, D.G.: Computing coastal ocean surface currents from infrared and ocean color satellite imagery. IEEE Trans. Geosci. Remote Sens. 45, 435–447 (2007)CrossRefGoogle Scholar
  8. Dohan, K., Maximenko, N.: Monitoring ocean currents with satellite sensors. Oceanography. 23(4), 94–103 (2010)CrossRefGoogle Scholar
  9. Emery, W.J., Collins, M.J., Crawford, W.R., Mackas, D.L.: An objective method for computing advective surface velocities from sequential infrared satellite images. Journal of Geophysical Research: Oceans. 91(C11), 12865–12878 (1986)CrossRefGoogle Scholar
  10. Emery, W.J., Fowler, C., Clayson, C.: Satellite-image-derived gulf stream currents compared with numerical model results. J. Atmos. Ocean. Technol. 9, 286–304 (1992)CrossRefGoogle Scholar
  11. Kelly, K.A., Strub, P.T.: Comparison of velocity estimates from advanced very high resolution radiometer in the coastal transition zone. Journal Geophysics Research. 97(96), 53–68 (1992)Google Scholar
  12. Kim, H.A., Park, K.A., Park, J.E.: Comparison of algorithms for sea surface current retrieval using Himawari-8/AHI data. Korean Journal of Remote Sensing. 32(6), 589–601 (2016) (in Korean with English abstract)CrossRefGoogle Scholar
  13. Kurihara, Y., Murakami, H., Kachi, M.: Sea surface temperature from the new Japanese geostationary meteorological Himawari-8 satellite. Geophys. Res. Lett. 43(3), 1234–1240 (2016)CrossRefGoogle Scholar
  14. Laurindo, L.C., Mariano, A.J., Lumpkin, R.: An improved near-surface velocity climatology for the global ocean from drifter observations. Deep-Sea Res. I Oceanogr. Res. Pap. 124, 73–92 (2017)CrossRefGoogle Scholar
  15. Lee, D.K., Niiler, P.P.: Surface circulation in the southwestern Japan/East Sea as observed from drifter and sea surface height. Deep-Sea Research Part I: Oceanographic Research Papers. 57(10), 1222–1232 (2010)CrossRefGoogle Scholar
  16. Lee, D.K., Lee, J.C., Lee, S.R., Lie, H.J.: A circulation study of the East Sea using satellite-tracked drifters 1: Tsushima current. J. Korean Fish. Soc. 30(6), 1021–1032 (1997)Google Scholar
  17. Leese, J.A., Novak, C.S., Clarke, B.B.: An automated technique for obtaining cloud motion from geosynchronous satellite data using cross-correlations. J. Appl. Meteorol. 10, 118–132 (1971)CrossRefGoogle Scholar
  18. Manabe, S.: Climate and the ocean circulation 1: I. the atmospheric circulation and the hydrology of the earth’s surface. Mon. Weather Rev. 97(11), 739–774 (1969)CrossRefGoogle Scholar
  19. Marcello, J., Eugenio, F., Marqués, F., Hernández-Guerra, F., Gasull, A.: Motion estimation techniques to automatically track oceanographic thermal structures in multi-sensor image sequences. IEEE Trans. Geosci. Remote Sens. 46(9), 2743–2762 (2008)CrossRefGoogle Scholar
  20. Matthews, D.K., Emery, W.J.: Velocity observations of the California current derived from satellite imagery. Journal of Geophysical Research: Oceans. 114(C8), (2009)Google Scholar
  21. Ninnis, R.M., Emery, W.J., Collins, M.J.: Automated extraction of pack ice motion from advanced very high resolution radiometer imagery. Journal of Geophysical Research: Oceans. 91(C9), 10725–10734 (1986)CrossRefGoogle Scholar
  22. Park, K.A., Lee, E.Y., Li, X., Chung, S.R., Sohn, E.H., Hong, S.: NOAA/AVHRR Sea surface temperature accuracy in the east/Japan Sea. International Journal of Digital Earth. 8(10), 784–804 (2015)CrossRefGoogle Scholar
  23. Pickard, G., Emery, W.: Descriptive Physical Oceanography: an Introduction, 5th edn. Butterworth Heinemann, Oxford (1990)Google Scholar
  24. Pond, S., Pickard, G.: Introductory Dynamical Oceanography, 2nd edn. Heinemann, Butterworth (2003)Google Scholar
  25. Primeau, F.: Characterizing transport between the surface mixed layer and the ocean interior with a forward and adjoint global ocean transport model. J. Phys. Oceanogr. 35(4), 545–564 (2005)CrossRefGoogle Scholar
  26. Qazi, W.A., Emery, W.J., Fox-Kemper, B.: Computing Ocean surface currents over the coastal California current system using 30-min-lag sequential SAR images. IEEE Trans. Geosci. Remote Sens. 52, 7559–7580 (2014)CrossRefGoogle Scholar
  27. Tokmakian, R., Strub, P.T., Mclean-Padman, J.: Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images. J. Atmos. Ocean. Technol. 7, 852–865 (1990)CrossRefGoogle Scholar
  28. Velden, C. S., and J. P. Dunion 2001: New satellite derived wind products and their applications to tropical cyclone/tropical wave forecasting. In 55th Interdepartmental Conf. Google Scholar
  29. Wahl, D.D., Simpson, J.J.: Physical processes affecting the objective determination of near-surface velocity from satellite data. Journal of Geophysical Research: Oceans. 95(C8), 13511–13528 (1990)CrossRefGoogle Scholar
  30. Wang, C., Deser, J. Y., Yu, P., DiNezio, and A. Clement, 2017: El Niño and Southern Oscillation (ENSO): a Review. In Coral reefs of the eastern tropical Pacific, Springer, Dordrecht, 85–106 pp.Google Scholar
  31. Warren, M., Quartly, G., Shutler, J., Miller, P., Yoshikawa, Y.: Estimation of ocean surface currents from maximum cross correlation applied to GOCI geostationary satellite remote sensing data over the Tsushima (Korea) straits. Journal of Geophysical Research: Oceans. 121, 6993–7009 (2016)Google Scholar
  32. Yang, H., Arnone, R., Jolliff, J.: Estimating advective near-surface currents from ocean color satellite images. Remote Sensing Environment. 158, 1–14 (2015)CrossRefGoogle Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2020

Authors and Affiliations

  1. 1.Department of Science EducationSeoul National UniversitySeoulSouth Korea
  2. 2.Department of Earth Science Education / Research Institute of Oceanography, College of EducationSeoul National UniversitySeoulSouth Korea
  3. 3.National Meteorological Satellite Center, Korea Meteorological AdministrationJincheonSouth Korea

Personalised recommendations