All Title Author
Keywords Abstract


Modeling Ocean Chlorophyll Distributions by Penalizing the Blending Technique

DOI: 10.4236/ojms.2014.41004, PP. 25-30

Keywords: In Situ, Satellite, Ship and Buoy, Penalized Regression Spline, Penalty, Penalized Blending

Full-Text   Cite this paper   Add to My Lib

Abstract:

Disparities between the in situ and satellite values at the positions where in situ values are obtained have been the main handicap to the smooth modeling of the distribution of ocean chlorophyll. The blending technique and the thin plate regression spline have so far been the main methods used in an attempt to calibrate ocean chlorophyll at positions where the in situ field could not provide value. In this paper, a combination of the two techniques has been used in order to provide improved and reliable estimates from the satellite field. The thin plate regression spline is applied to the blending technique by imposing a penalty on the differences between the satellite and in situ fields at positions where they both have observations. The objective of maximizing the use of the satellite field for prediction was outstanding in a validation study where the penalized blending method showed a remarkable improvement in its estimation potentials. It is hoped that most analysis on primary productivity and management in the ocean environment will be greatly affected by this result, since chlorophyll is one of the most important components in the formation of the ocean life cycle.

References

[1]  E. Clarke, D. Speirs, M. Heath, S. Wood, W. Gurney and S. Holmes, “Calibrating Remotely Sensed Chlorophyll-a Data by Using Penalized Regression Splines,” Journal of Royal Statistics Society, Series C, Vol. 55, No. 3, 2006, pp. 331-353.
[2]  R. W. Eppley, E. Stewart, M. R. Abbott and U. Heyman, “Estimating Ocean Primary Production from Satellite Chlorophyll. Introduction to Regional Differences and Statistics for the Southern California Bight,” Journal of Plankton Research, Vol. 7, No. 1, 1985, pp. 57-70.
http://dx.doi.org/10.1093/plankt/7.1.57
[3]  D. A. Flemer, “Chlorophyll Analysis as a Method of Evaluating the Standing Crop Phytoplankton and Primary Productivity,” Chesapeake Science, Vol. 10, No. 3-4, 1969, pp. 301-306. http://dx.doi.org/10.2307/1350474
[4]  A. H. Oort, “Global Atmospheric Circulation Statistics,” NOAA Prof Paper 14 180pp Nat. Oceanic and Atmospheric Administration Silver Spring, Maryland, 1983.
[5]  R. W. Reynolds, “A Real-Time Global Sea Surface Temperature Analysis,” Journal of Climate, Vol. 1, No. 1, 1988, pp. 75-87.
http://dx.doi.org/10.1175/1520-0442(1988)001<0075:ARTGSS>2.0.CO;2
[6]  W. W. Gregg and M. E. Conkright, “Global Seasonal Climatologies of Ocean Chlorophyll: Blending in Situ and Satellite Data for Coastal Zone Colour Scanner Era,” Journal of Geophysical Research, Vol. 106, No. C2, 2001, pp. 2499-2515. http://dx.doi.org/10.1029/1999JC000028
[7]  M. A. Onabid, “Improved Ocean Chlorophyll Estimate from Remote Sensed Data: The Modified Blending Technique,” African Journal of Environmental Science and Technology, Vol. 5, No. 9, 2001, pp. 732-747.
[8]  S. N. Wood, “Thin Plate Regression Splines,” Royal Statistical Society, Series B, Vol. 65, No. 1, 2003, pp. 95-114.
http://dx.doi.org/10.1111/1467-9868.00374
[9]  S. N. Wood, “Generalized Additive Models: An Introduction with R,” Chapman and Hall/CRC, London, 2006.
[10]  R. Scraton, “Further Numerical Methods in Basic,” Edward Arnold Ltd, Kent, 1987.

Full-Text

comments powered by Disqus