Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
GMD | Articles | Volume 13, issue 3
Geosci. Model Dev., 13, 1609–1622, 2020
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 1609–1622, 2020
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.

Model code and software

DINCAE Alexander Barth https://doi.org/10.5281/zenodo.3691745

Publications Copernicus
Download
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.
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network....
Citation