Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1609-2020
https://doi.org/10.5194/gmd-13-1609-2020
Model description paper
 | 
27 Mar 2020
Model description paper |  | 27 Mar 2020

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

Alexander Barth, Aida Alvera-Azcárate, Matjaz Licer, and Jean-Marie Beckers

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Cited articles

Alvera-Azcárate, A., Barth, A., Rixen, M., and Beckers, J.-M.: Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea Surface Temperature, Ocean Model., 9, 325–346, https://doi.org/10.1016/j.ocemod.2004.08.001, 2005. a, b, c, d
Alvera-Azcárate, A., Barth, A., Beckers, J.-M., and Weisberg, R. H.: Multivariate reconstruction of missing data in sea surface temperature, chlorophyll and wind satellite field, J. Geophys. Res., 112, C03008, https://doi.org/10.1029/2006JC003660, 2007. a
Alvera-Azcárate, A., Barth, A., Sirjacobs, D., and Beckers, J.-M.: Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF, Ocean Sci., 5, 475–485, https://doi.org/10.5194/os-5-475-2009, 2009. a, b
Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M.: Analysis of SMOS sea surface salinity data using DINEOF, Remote Sens. Environ., 180, 137–145, https://doi.org/10.1016/j.rse.2016.02.044, 2016. a
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Short summary
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images.