Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2183-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2183-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 Mar 2022
Development and technical paper |
| 15 Mar 2022
| 15 Mar 2022
DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations
GHER, University of Liège, Liège, Belgium
Aida Alvera-Azcárate
GHER, University of Liège, Liège, Belgium
Charles Troupin
GHER, University of Liège, Liège, Belgium
Jean-Marie Beckers
GHER, University of Liège, Liège, Belgium
Viewed
Total article views: 3,543 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Nov 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,520 | 941 | 82 | 3,543 | 70 | 56 |
- HTML: 2,520
- PDF: 941
- XML: 82
- Total: 3,543
- BibTeX: 70
- EndNote: 56
Total article views: 2,387 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Mar 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,649 | 678 | 60 | 2,387 | 62 | 50 |
- HTML: 1,649
- PDF: 678
- XML: 60
- Total: 2,387
- BibTeX: 62
- EndNote: 50
Total article views: 1,156 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Nov 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 871 | 263 | 22 | 1,156 | 8 | 6 |
- HTML: 871
- PDF: 263
- XML: 22
- Total: 1,156
- BibTeX: 8
- EndNote: 6
Viewed (geographical distribution)
Total article views: 3,543 (including HTML, PDF, and XML)
Thereof 3,377 with geography defined
and 166 with unknown origin.
Total article views: 2,387 (including HTML, PDF, and XML)
Thereof 2,303 with geography defined
and 84 with unknown origin.
Total article views: 1,156 (including HTML, PDF, and XML)
Thereof 1,074 with geography defined
and 82 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Application of Synthetic DINCAE–BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll–a from Satellite Observations in the Arabian Sea X. Yan et al. 10.3390/jmse11040743
- Enhanced Reconstruction of Satellite-Derived Monthly Chlorophyll a Concentration With Fourier Transform Convolutional-LSTM S. Chen et al. 10.1109/TGRS.2024.3394399
- Estimation of daytime all-sky sea surface temperature from Himawari-8 based on multilayer stacking machine learning H. He et al. 10.1016/j.jag.2024.104055
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. 10.3390/jmse11020340
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al. 10.3389/fmars.2024.1396322
- A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information G. Zhou et al. 10.1016/j.jag.2024.103724
- Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. 10.1080/15481603.2023.2166377
- Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
- Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies S. Martin et al. 10.1029/2022MS003589
- Self-Supervised Spatiotemporal Imputation Model for Highly Sparse Chl-a Data via Fusing Multisource Satellite Data S. Wang et al. 10.1109/TGRS.2024.3440912
- A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou 10.3390/jmse11040810
- End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. 10.3390/rs14164024
- Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam N. Binh et al. 10.1016/j.jag.2022.102951
- Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations E. Jang et al. 10.1016/j.jag.2024.104029
- DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network X. Guo et al. 10.1088/1742-6596/2816/1/012004
- Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li 10.1109/TGRS.2023.3335940
- NCDatasets.jl: a Julia package for manipulating netCDF data sets A. Barth 10.21105/joss.06504
17 citations as recorded by crossref.
- Application of Synthetic DINCAE–BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll–a from Satellite Observations in the Arabian Sea X. Yan et al. 10.3390/jmse11040743
- Enhanced Reconstruction of Satellite-Derived Monthly Chlorophyll a Concentration With Fourier Transform Convolutional-LSTM S. Chen et al. 10.1109/TGRS.2024.3394399
- Estimation of daytime all-sky sea surface temperature from Himawari-8 based on multilayer stacking machine learning H. He et al. 10.1016/j.jag.2024.104055
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. 10.3390/jmse11020340
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al. 10.3389/fmars.2024.1396322
- A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information G. Zhou et al. 10.1016/j.jag.2024.103724
- Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. 10.1080/15481603.2023.2166377
- Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
- Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies S. Martin et al. 10.1029/2022MS003589
- Self-Supervised Spatiotemporal Imputation Model for Highly Sparse Chl-a Data via Fusing Multisource Satellite Data S. Wang et al. 10.1109/TGRS.2024.3440912
- A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou 10.3390/jmse11040810
- End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. 10.3390/rs14164024
- Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam N. Binh et al. 10.1016/j.jag.2022.102951
- Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations E. Jang et al. 10.1016/j.jag.2024.104029
- DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network X. Guo et al. 10.1088/1742-6596/2816/1/012004
- Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li 10.1109/TGRS.2023.3335940
1 citations as recorded by crossref.
Latest update: 19 Sep 2024
Short summary
Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Earth-observing satellites provide routine measurement of several ocean parameters. However,...
