Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. 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-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations
GeoHydrodynamics and Environment Research (GHER), University of Liège, Liège, Belgium
Aida Alvera-Azcárate
GeoHydrodynamics and Environment Research (GHER), University of Liège, Liège, Belgium
Matjaz Licer
National Institute of Biology, Marine Biology Station, Piran, Slovenia
Jean-Marie Beckers
GeoHydrodynamics and Environment Research (GHER), University of Liège, Liège, Belgium
Viewed
Total article views: 6,821 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Jun 2019)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
4,886 | 1,829 | 106 | 6,821 | 98 | 86 |
- HTML: 4,886
- PDF: 1,829
- XML: 106
- Total: 6,821
- BibTeX: 98
- EndNote: 86
Total article views: 5,551 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Mar 2020)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
4,055 | 1,411 | 85 | 5,551 | 89 | 74 |
- HTML: 4,055
- PDF: 1,411
- XML: 85
- Total: 5,551
- BibTeX: 89
- EndNote: 74
Total article views: 1,270 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Jun 2019)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
831 | 418 | 21 | 1,270 | 9 | 12 |
- HTML: 831
- PDF: 418
- XML: 21
- Total: 1,270
- BibTeX: 9
- EndNote: 12
Viewed (geographical distribution)
Total article views: 6,821 (including HTML, PDF, and XML)
Thereof 6,116 with geography defined
and 705 with unknown origin.
Total article views: 5,551 (including HTML, PDF, and XML)
Thereof 5,023 with geography defined
and 528 with unknown origin.
Total article views: 1,270 (including HTML, PDF, and XML)
Thereof 1,093 with geography defined
and 177 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
68 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
- Iterative spatial leave-one-out cross-validation and gap-filling based data augmentation for supervised learning applications in marine remote sensing A. Stock & A. Subramaniam 10.1080/15481603.2022.2107113
- Can deep learning beat numerical weather prediction? M. Schultz et al. 10.1098/rsta.2020.0097
- Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution? B. Ping et al. 10.3390/rs13183568
- Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables M. Li et al. 10.1007/s13131-021-1734-1
- Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. 10.1109/JAS.2023.123537
- 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
- PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al. 10.5194/gmd-17-7347-2024
- A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet H. Ye et al. 10.5194/essd-16-3125-2024
- Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. 10.3390/jmse11020340
- A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou 10.3390/jmse11040810
- Deep learning for ocean temperature forecasting: a survey X. Zhao et al. 10.1007/s44295-024-00042-3
- Inpainting of cloud-occlusion sea surface temperature image from a novel generative network using multi-scale physical constraints Y. Diao et al. 10.1007/s11042-024-20231-w
- Super-resolution of subsurface temperature field from remote sensing observations based on machine learning H. Su et al. 10.1016/j.jag.2021.102440
- Reconstruction of the Basin‐Wide Sea‐Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks Z. Zhang et al. 10.1029/2020JC016402
- Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks H. Su et al. 10.1016/j.rse.2021.112465
- Reconstruction of Daily MODIS/Aqua Chlorophyll-a Concentration in Turbid Estuarine Waters Based on Attention U-NET H. Ye et al. 10.3390/rs15030546
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning J. Wang et al. 10.3390/rs12172858
- 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
- Accurate reconstruction of satellite-derived SST under cloud and cloud-free areas using a physically-informed machine learning approach C. Young et al. 10.1016/j.rse.2024.114339
- Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives M. Buzzicotti 10.1209/0295-5075/acc88c
- CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al. 10.3389/fmars.2024.1151868
- Comparison of Cloud-Filling Algorithms for Marine Satellite Data A. Stock et al. 10.3390/rs12203313
- Investigating ocean surface responses to typhoons using reconstructed satellite data C. Ji et al. 10.1016/j.jag.2021.102474
- Satellite observations estimating the effects of river discharge and wind‐driven upwelling on phytoplankton dynamics in the Chesapeake Bay N. Nezlin et al. 10.1002/ieam.4597
- 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
- Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry W. Li et al. 10.3390/rs14194904
- Ocean Currents Reconstruction from a Combination of Altimeter and Ocean Colour Data: A Feasibility Study D. Ciani et al. 10.3390/rs13122389
- Inversion of the three-dimensional temperature structure of mesoscale eddies in the Northwest Pacific based on deep learning F. Yu et al. 10.1007/s13131-021-1841-z
- Downscaling of ocean fields by fusion of heterogeneous observations using Deep Learning algorithms S. Thiria et al. 10.1016/j.ocemod.2023.102174
- Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method C. Gu et al. 10.3390/s22155600
- A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data Z. Li et al. 10.3390/rs16101745
- MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion E. Goh et al. 10.5194/os-20-1309-2024
- Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations L. Meng et al. 10.1029/2021JC017605
- Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. 10.1080/15481603.2023.2166377
- Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data J. Vient et al. 10.3390/rs13173537
- High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension S. Jung et al. 10.3390/rs14030575
- A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction G. Zhou et al. 10.1016/j.jag.2024.103815
- Spatiotemporal Fusion Network Based on Improved Transformer for Inverting Subsurface Thermohaline Structure J. Mu et al. 10.1109/TGRS.2024.3446805
- Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific M. Xing et al. 10.1016/j.scitotenv.2022.156981
- Predicting particle catchment areas of deep-ocean sediment traps using machine learning T. Picard et al. 10.5194/os-20-1149-2024
- Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. 10.1002/joc.7926
- Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al. 10.1109/TGRS.2023.3268006
- 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
- STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data S. Wang et al. 10.3390/rs15010088
- Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
- Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al. 10.3390/rs14051159
- Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu 10.3390/ijgi13050162
- Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment T. Zhang et al. 10.3390/fishes9030081
- Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set T. Wang et al. 10.1109/TGRS.2021.3067646
- End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. 10.3390/rs14164024
- 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
- 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
- Mitigating Masked Pixels in a Climate-Critical Ocean Dataset A. Agabin et al. 10.3390/rs16132439
- Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields R. Fablet et al. 10.3389/fams.2021.655224
- Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni 10.1109/ACCESS.2021.3122237
- A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al. 10.1080/01431161.2021.1957513
- Impact of Parameterized Isopycnal Diffusivity on Shelf‐Ocean Exchanges Under Upwelling‐Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network C. Xie et al. 10.1029/2022MS003424
- Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir S. Mukonza & J. Chiang 10.3390/w14182935
- CCGAN as a Tool for Satellite-Derived Chlorophyll a Concentration Gap Reconstruction L. Ćatipović et al. 10.3390/jmse11091814
- A Global Ocean Oxygen Database and Atlas for Assessing and Predicting Deoxygenation and Ocean Health in the Open and Coastal Ocean M. Grégoire et al. 10.3389/fmars.2021.724913
- Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring S. Mukonza & J. Chiang 10.3390/environments10100170
- Reconstruction of chlorophyll-a satellite data in Bohai and Yellow sea based on DINCAE method X. Luo et al. 10.1080/01431161.2022.2090872
- Application of DINCAE to Reconstruct the Gaps in Chlorophyll-a Satellite Observations in the South China Sea and West Philippine Sea Z. Han et al. 10.3390/rs12030480
- 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry M. Beauchamp et al. 10.5194/gmd-16-2119-2023
65 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
- Iterative spatial leave-one-out cross-validation and gap-filling based data augmentation for supervised learning applications in marine remote sensing A. Stock & A. Subramaniam 10.1080/15481603.2022.2107113
- Can deep learning beat numerical weather prediction? M. Schultz et al. 10.1098/rsta.2020.0097
- Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution? B. Ping et al. 10.3390/rs13183568
- Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables M. Li et al. 10.1007/s13131-021-1734-1
- Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. 10.1109/JAS.2023.123537
- 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
- PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al. 10.5194/gmd-17-7347-2024
- A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet H. Ye et al. 10.5194/essd-16-3125-2024
- Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. 10.3390/jmse11020340
- A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou 10.3390/jmse11040810
- Deep learning for ocean temperature forecasting: a survey X. Zhao et al. 10.1007/s44295-024-00042-3
- Inpainting of cloud-occlusion sea surface temperature image from a novel generative network using multi-scale physical constraints Y. Diao et al. 10.1007/s11042-024-20231-w
- Super-resolution of subsurface temperature field from remote sensing observations based on machine learning H. Su et al. 10.1016/j.jag.2021.102440
- Reconstruction of the Basin‐Wide Sea‐Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks Z. Zhang et al. 10.1029/2020JC016402
- Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks H. Su et al. 10.1016/j.rse.2021.112465
- Reconstruction of Daily MODIS/Aqua Chlorophyll-a Concentration in Turbid Estuarine Waters Based on Attention U-NET H. Ye et al. 10.3390/rs15030546
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning J. Wang et al. 10.3390/rs12172858
- 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
- Accurate reconstruction of satellite-derived SST under cloud and cloud-free areas using a physically-informed machine learning approach C. Young et al. 10.1016/j.rse.2024.114339
- Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives M. Buzzicotti 10.1209/0295-5075/acc88c
- CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al. 10.3389/fmars.2024.1151868
- Comparison of Cloud-Filling Algorithms for Marine Satellite Data A. Stock et al. 10.3390/rs12203313
- Investigating ocean surface responses to typhoons using reconstructed satellite data C. Ji et al. 10.1016/j.jag.2021.102474
- Satellite observations estimating the effects of river discharge and wind‐driven upwelling on phytoplankton dynamics in the Chesapeake Bay N. Nezlin et al. 10.1002/ieam.4597
- 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
- Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry W. Li et al. 10.3390/rs14194904
- Ocean Currents Reconstruction from a Combination of Altimeter and Ocean Colour Data: A Feasibility Study D. Ciani et al. 10.3390/rs13122389
- Inversion of the three-dimensional temperature structure of mesoscale eddies in the Northwest Pacific based on deep learning F. Yu et al. 10.1007/s13131-021-1841-z
- Downscaling of ocean fields by fusion of heterogeneous observations using Deep Learning algorithms S. Thiria et al. 10.1016/j.ocemod.2023.102174
- Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method C. Gu et al. 10.3390/s22155600
- A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data Z. Li et al. 10.3390/rs16101745
- MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion E. Goh et al. 10.5194/os-20-1309-2024
- Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations L. Meng et al. 10.1029/2021JC017605
- Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. 10.1080/15481603.2023.2166377
- Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data J. Vient et al. 10.3390/rs13173537
- High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension S. Jung et al. 10.3390/rs14030575
- A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction G. Zhou et al. 10.1016/j.jag.2024.103815
- Spatiotemporal Fusion Network Based on Improved Transformer for Inverting Subsurface Thermohaline Structure J. Mu et al. 10.1109/TGRS.2024.3446805
- Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific M. Xing et al. 10.1016/j.scitotenv.2022.156981
- Predicting particle catchment areas of deep-ocean sediment traps using machine learning T. Picard et al. 10.5194/os-20-1149-2024
- Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. 10.1002/joc.7926
- Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al. 10.1109/TGRS.2023.3268006
- 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
- STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data S. Wang et al. 10.3390/rs15010088
- Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
- Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al. 10.3390/rs14051159
- Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu 10.3390/ijgi13050162
- Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment T. Zhang et al. 10.3390/fishes9030081
- Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set T. Wang et al. 10.1109/TGRS.2021.3067646
- End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. 10.3390/rs14164024
- 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
- 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
- Mitigating Masked Pixels in a Climate-Critical Ocean Dataset A. Agabin et al. 10.3390/rs16132439
- Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields R. Fablet et al. 10.3389/fams.2021.655224
- Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni 10.1109/ACCESS.2021.3122237
- A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al. 10.1080/01431161.2021.1957513
- Impact of Parameterized Isopycnal Diffusivity on Shelf‐Ocean Exchanges Under Upwelling‐Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network C. Xie et al. 10.1029/2022MS003424
- Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir S. Mukonza & J. Chiang 10.3390/w14182935
- CCGAN as a Tool for Satellite-Derived Chlorophyll a Concentration Gap Reconstruction L. Ćatipović et al. 10.3390/jmse11091814
- A Global Ocean Oxygen Database and Atlas for Assessing and Predicting Deoxygenation and Ocean Health in the Open and Coastal Ocean M. Grégoire et al. 10.3389/fmars.2021.724913
- Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring S. Mukonza & J. Chiang 10.3390/environments10100170
3 citations as recorded by crossref.
- Reconstruction of chlorophyll-a satellite data in Bohai and Yellow sea based on DINCAE method X. Luo et al. 10.1080/01431161.2022.2090872
- Application of DINCAE to Reconstruct the Gaps in Chlorophyll-a Satellite Observations in the South China Sea and West Philippine Sea Z. Han et al. 10.3390/rs12030480
- 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry M. Beauchamp et al. 10.5194/gmd-16-2119-2023
Latest update: 20 Nov 2024
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....