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Computational Intelligence and Neuroscience
Volume 2016 (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.


A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN’s generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.