Reconstruction of the surface temperature fields according to the fragmentary data of remote sensing

Reconstruction of the surface temperature fields according to the fragmentary data of remote sensing We propose a gap-filling method for the data of remote sensing of the hydrophysical and biological characteristics of the water surface. The proposed method of reconstruction is based on the representation of the fields of surface characteristics as the sums of certain numbers of empirical orthogonal functions (EOF) making the largest contributions to the total variance of the field. According to the fragmentary data obtained as a result of processing of the satellite images for the summer season, we construct estimates of the mean field and of the four-dimensional space covariance function of the surface temperature of the Black Sea. The coefficients of expansion are computed by the method of least squares or determined with the help of a genetic searching algorithm. The results of numerical experiments show that the proposed method is quite promising for applications in the problems of gap filling in the available satellite data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Oceanography Springer Journals

Reconstruction of the surface temperature fields according to the fragmentary data of remote sensing

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Publisher
Springer US
Copyright
Copyright © 2011 by Springer Science+Business Media, Inc.
Subject
Earth Sciences; Oceanography; Remote Sensing/Photogrammetry; Atmospheric Sciences; Climate Change; Environmental Physics
ISSN
0928-5105
eISSN
0928-5105
D.O.I.
10.1007/s11110-011-9115-5
Publisher site
See Article on Publisher Site

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