Advertisement

Journal of Ocean University of China

, Volume 15, Issue 3, pp 399–406 | Cite as

Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

  • Hong Zhao
  • Changjun Li
  • Hongping Li
  • Kebo Lv
  • Qinghui Zhao
Article

Abstract

The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°–25°N, 105°–125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Key words

sea surface salinity retrieved algorithm SMOS principle component regression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boutin, J., and Martin, N., 2006. ARGO upper salinity measurements: Perspectives for L-band radiometers calibration and retrieved sea surface salinity validation. IEEE Geoscience and Remote Sensing Letters, 3 (2): 202–206.CrossRefGoogle Scholar
  2. Boutin, J., Martin, N., Reverdin, G., Yin, X., and Gaillard, F., 2013. Sea surface freshening inferred from SMOS and ARGO salinity: Impact of rain. Ocean Science, 9: 183–192.CrossRefGoogle Scholar
  3. Boutin, J., Martin, N., Yin, X., Font, J., Reul, N., and Spurgeon, P., 2012. First assessment of SMOS data over open ocean: Part II–Sea surface salinity. IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1662–1675.CrossRefGoogle Scholar
  4. Camps, A., Vall-llossera, M., Duffo, N., Torres, F., and Corbella, I., 2005. Performance of sea surface salinity and soil moisture retrieval algorithms with different auxiliary datasets in 2-D L-Band aperture synthesis interferometric radiometers. IEEE Transactions on Geoscience and Remote Sensing, 43 (5): 1189–1200.CrossRefGoogle Scholar
  5. Camps, A., Vall-llossera, M., Miranda, J., and Font, J., 2004. Sea surface brightness temperature at L-band: Impact of surface currents. Geoscience and Remote Sensing Symposium, 5: 3481–3484.Google Scholar
  6. Feng, S. Z., Li, F. Q., and Li, S. J., 1999. Introduction to Marine Science. Higher Education Press, Beijing, 503pp.Google Scholar
  7. Font, J., Camps, A., Borges, A., Martín-Neira, M., Boutin, J., Reul, N., Kerr, Y., Hahne, A., and Mecklenburg, S., 2010. SMOS: The challenging measurement of sea surface salinity from space. Proceedings of the IEEE, 98 (5): 649–665.CrossRefGoogle Scholar
  8. Gabarró, C., Portabella, M., Talone, M., and Font, J., 2009. Toward an optimal SMOS ocean salinity inversion algorithm. IEEE Geoscience and Remote Sensing Letters, 6 (3): 509–513.CrossRefGoogle Scholar
  9. Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M. J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martin-Neira, M., and Mecklenburg, S., 2010. The SMOS mission: New tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98: 666–687.CrossRefGoogle Scholar
  10. Marghany, M., 2009. Linear algorithm for salinity distribution modelling from MODIS data. Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009. 3: 365–368.Google Scholar
  11. Marghany, M., 2010. Examining the least square method to retrieve sea surface salinity from MODIS satellite data. The European Journal of Social Science Research, 40 (30): 377–386.Google Scholar
  12. Marghany, M., and Hashim, M., 2011a. A numerical method for retrieving sea surface salinity from MODIS satellite data. International Journal of the Physical Sciences, 6 (13): 3116–3125.Google Scholar
  13. Marghany, M., and Hashim, M., 2011b. Retrieving seasonal sea surface salinity from MODIS satellite data using a Box-Jenkins algorithm. Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, 2017–2020.CrossRefGoogle Scholar
  14. Mecklenburg, S., Drusch, M., Kerr, Y. H., Font, J., Martín-Neira, M., Delwart, S., Buenadicha, G., Reul, N., Daganzo-Eusebio, E., Oliva, R., and Crapolicchio, R., 2012. ESA’s soil moisture and ocean salinity mission: Mission performance and operations. IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1354–1366.CrossRefGoogle Scholar
  15. Misra, S., Mohammed, P. N., Güner, B., Ruf, C. S., Piepmeier, J. R., and Johnson, J. T., 2009. Microwave radiometer radiofrequency interference detection algorithms: A comparative study. IEEE Transactions on Geoscience and Remote Sensing, 47 (11): 3742–3754.CrossRefGoogle Scholar
  16. Qing, S., Zhang, J., Cui, T. W., and Bao, Y. H., 2012. Remote sensing retrieval of total absorption coefficient in the Bohai Sea. Chinese Journal of Oceanology and Limnology, 30 (5): 806–813.CrossRefGoogle Scholar
  17. Qing, S., Zhang, J., Cui, T. W., and Bao, Y. H., 2013. Retrieval of sea surface salinity with MERIS and MODIS data in the Bohai Sea. Remote Sensing of Environment, 136: 117–125.CrossRefGoogle Scholar
  18. Reul, N., Tenerelli, J., Floury, N., and Chapron, B., 2008. Earth viewing L-band radiometer sensing of sea surface scattered celestial sky radiation. Part II: Application to SMOS. IEEE Transactions on Geoscience and Remote Sensing, 46 (3): 659–674.Google Scholar
  19. Sabia, R., Camps, A., Vall-llossera, M., and Reul, N., 2006. Impact on sea surface salinity retrieval of different auxiliary data within the SMOS mission. IEEE Transactions Geoscience and Remote Sensing, 44 (10): 2769–2778.CrossRefGoogle Scholar
  20. Talone, M., Camps, A., Mourre, B., Sabia, R., Vall-llossera, M., Gourrion, J., Gabarró, G., and Font, J., 2009. Simulated SMOS levels 2 and 3 products: The effect of introducing ARGO data in the processing chain and its impact on the error induced by the vicinity of the coast. IEEE Transactions on Geoscience and Remote Sensing, 47 (9): 3041–3050.CrossRefGoogle Scholar
  21. Urquhart, E. A., Zaitchik, B. F., Hoffman, M. J., Guikema, S. D., and Geiger, E. F., 2012. Remotely sensed estimates of surface salinity in the Chesapeake Bay: A statistical approach. Remote Sensing of Environment, 123: 522–531.CrossRefGoogle Scholar
  22. Wong, M., Kwan, S. H. L., Young, J. K., Nichol, J., Zhang, G. L., and Emerson, N., 2007. Modeling of suspended solids and sea surface salinity in Hong Kong using Aqua/MODIS satellite images. Korean Journal of Remote Sensing, 23 (3): 161–169.Google Scholar
  23. Yin, X., Boutin, J., Martin, N., and Spurgeon, P., 2012. Optimization of L-band sea surface emissivity models deduced from SMOS data. IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1414–1426.CrossRefGoogle Scholar
  24. Yin, X., Boutin, J., Martin, N., Spurgeon, P., Vergely, J., and Gaillard, F., 2014. Errors in SMOS Sea Surface Salinity and their dependency on a priori wind speed. Remote Sensing of Environment, 146: 159–171.CrossRefGoogle Scholar
  25. Yueh, S., West, R., Wilson, W., Li, F., Njoku, E., and Rahmat-Samii, Y., 2001. Error sources and feasibility formicrowave remote sensing of ocean surface salinity. IEEE Transactions on Geoscience and Remote Sensing, 39 (5): 1049–1060.CrossRefGoogle Scholar
  26. Zine, S., Boutin, J., Font, J., Reul, N., Waldteufel, P., Gabarró, C., Tenerelli, J., Petitcolin, F., Vergely, J., Talone, M., and Delwart, S., 2008. Overview of the SMOS sea surface salinity prototype processor. IEEE Transactions on Geoscience and Remote Sensing, 46 (3): 621–644.CrossRefGoogle Scholar

Copyright information

© Science Press, Ocean University of China and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hong Zhao
    • 1
  • Changjun Li
    • 1
  • Hongping Li
    • 2
  • Kebo Lv
    • 1
  • Qinghui Zhao
    • 1
  1. 1.School of Mathematical SciencesOcean University of ChinaQingdaoP. R. China
  2. 2.Department of Marine Technology, College of Information Science and EngineeringOcean University of ChinaQingdaoP. R. China

Personalised recommendations