title: Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.
abstract: 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.
authors.affiliation: NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA.
authors.forename: Vladimir
authors.identifier: 0000-0003-0848-4161
authors.initials: V
authors.lastname: Krasnopolsky
authors.affiliation: NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA.
authors.forename: Sudhir
authors.initials: S
authors.lastname: Nadiga
authors.affiliation: NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA.
authors.forename: Avichal
authors.initials: A
authors.lastname: Mehra
authors.affiliation: NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Bayler
authors.affiliation: NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA.
authors.forename: David
authors.initials: D
authors.lastname: Behringer
mesh_terms: D000465:Algorithms; D002734:Chlorophyll; D000077194:Chlorophyll A; D003116:Color; D003124:Colorimetry / Q000379:methods; D004784:Environmental Monitoring; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D009792:Oceans and Seas; D015203:Reproducibility of Results; D063809:Satellite Imagery
chemical_list: D002734:Chlorophyll; D000077194:Chlorophyll A
references.citation: Neural Netw. 2007 May;20(4):454-61
references.pmid: 17521879