Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016, Article ID 6156513, 9 pages
Research Article

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA

Received 6 August 2015; Revised 23 October 2015; Accepted 26 October 2015

Academic Editor: José Alfredo Hernandez

Copyright © 2016 Vladimir Krasnopolsky et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Camps-Valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis, Wiley Online Library, 2009.
  2. V. Krasnopolsky, The Application of Neural Networks in the Earth System Sciences. Neural Networks Emulations for Complex Multidimensional Mappings, Springer, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  3. O. S. Hidalgo, J. C. Nieto, C. Cunha, and C. Guedes Soares, “Filling missing observations in time series of significant wave height,” in Proceedings of the 14th International Conference on Offshore Mechanics and Arctic Engineering (OMAE '95), C. Guedes Soares, Ed., vol. 2, pp. 9–17, ASME, Copenhagen, Denmark, June 1995.
  4. F. Arena and S. Puca, “The reconstruction of significant wave height time series by using a neural network approach,” Journal of Offshore Mechanics and Arctic Engineering, vol. 126, no. 3, pp. 213–219, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. O. Makarynskyy and D. Makarynska, “Wave prediction and data supplementation with artificial neural networks,” Journal of Coastal Research, vol. 23, no. 4, pp. 951–960, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Peres, C. Iuppa, L. Cavallaro, A. Cancelliere, and E. Foti, “Significant wave height record extension by neural networks and reanalysis wind data,” Ocean Modelling, vol. 94, pp. 128–140, 2015. View at Publisher · View at Google Scholar
  7. C. B. Miller and P. A. Wheeler, Biological Oceanography, Wiley-Blackwell, 2nd edition, 2012.
  8. W. W. Gregg, “Tracking the SeaWiFS record with a coupled physical/biogeochemical/radiative model of the global oceans,” Deep Sea Research Part II: Topical Studies in Oceanography, vol. 49, no. 1–3, pp. 81–105, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Mehra and I. Rivin, “A real time operational global ocean forecast system,” in Proceedings of the US GODAE OceanView Workshop on Observing System Evaluation and Intercomparisons, University of California, Santa Cruz, Calif, USA, June 2011,
  10. D. W. Behringer, “The global ocean data assimilation system at NCEP,” in Proceedings of the 11th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, AMS 87th Annual Meeting, p. 12, San Antonio, Tex, USA, 2007.
  11. S. Saha, S. Moorthi, X. Wu et al., “The NCEP climate forecast system version 2,” Journal of Climate, vol. 27, no. 6, pp. 2185–2208, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Dzwonkowski and X.-H. Yan, “Development and application of a neural network based ocean colour algorithm in coastal waters,” International Journal of Remote Sensing, vol. 26, no. 6, pp. 1175–1200, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. E. J. Kwiatkowska and G. S. Fargion, “Merger of ocean color information from multiple satellite missions under the NASA SIMBIOS Project Office,” in Proceedings of the 5th International Conference on Information Fusion (FUSION '02), vol. 1, pp. 291–298, Annapolis, Md, USA, July 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. W. W. Hsieh, Machine Learning Methods in the Environmental Sciences, Cambridge University Press, Cambridge, UK, 2009. View at Publisher · View at Google Scholar
  15. S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, New York, NY, USA, 1994.
  16. V. M. Krasnopolsky, “Reducing uncertainties in neural network Jacobians and improving accuracy of neural network emulations with NN ensemble approaches,” Neural Networks, vol. 20, no. 4, pp. 454–461, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. N. Baker, Joint Polar Satellite Systems (JPSS), VIIRS Ocean Color/Chlorophyll Algorithm Theoretical Basis Document ATBD, GSFC, Greenbelt, Md, USA, 2011,
  18. K. V. Lebedev, S. DeCarlo, P. W. Hacker, N. A. Maximenko, J. T. Potemra, and Y. Shen, “Argo products at the asia-pacific data-research center,” Eos, Transactions American Geophysical Union, vol. 91, no. 26, 2010, Ocean Sciences Meeting Supplement, Abstract IT25A-01, 2010. View at Google Scholar
  19. E. W. Leuliette, R. S. Nerem, and G. T. Mitchum, “Calibration of TOPEX/Poseidon and Jason altimeter data to construct a continuous record of mean sea level change,” Marine Geodesy, vol. 27, no. 1-2, pp. 79–94, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. R. W. Reynolds, T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, “Daily high-resolution-blended analyses for sea surface temperature,” Journal of Climate, vol. 20, no. 22, pp. 5473–5496, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Tang, S. H. Yueh, A. G. Fore, and A. Hayashi, “Validation of Aquarius sea surface salinity with in situ measurements from Argo floats and moored buoys,” Journal of Geophysical Research: Oceans, vol. 119, no. 9, pp. 6171–6189, 2014. View at Publisher · View at Google Scholar
  22. AQUARIUS USER GUIDE, Aquarius Dataset Version 3.0, Document #s: JPL D-70012 AQ-010-UG-0008, JPL URS CL#: 14-0748, 2014,