Articles | Volume 14, issue 4
https://doi.org/10.5194/tc-14-1289-2020
© 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/tc-14-1289-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2020
Research article |
| 21 Apr 2020
| 21 Apr 2020
Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2
Angela Cheng
CORRESPONDING AUTHOR
Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Barbara Casati
Recherche en Prévision Numérique Environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
Adrienne Tivy
Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada
Tom Zagon
Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada
Jean-François Lemieux
Recherche en Prévision Numérique Environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
L. Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
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Cited
17 citations as recorded by crossref.
- Intercomparison of Arctic Sea Ice Backscatter and Ice Type Classification Using Ku-Band and C-Band Scatterometers Z. Zhang et al. 10.1109/TGRS.2021.3099835
- AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting A. Kucik & A. Stokholm 10.1038/s41598-023-32467-x
- Mapping the thickness of sea ice in the Arctic as an example of using data from a ship-based television complex for operational hydrometeorological support of maritime activities E. Afanasyeva et al. 10.30758/0555-2648-2022-68-2-96-117
- Potential of Lightweight Drones and Object-Oriented Image Segmentation in Forest Plantation Assessment J. Dixit et al. 10.3390/rs16091554
- Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification J. Lohse et al. 10.3390/rs13040552
- Coastal Sea Ice Concentration Derived from Marine Radar Images: A Case Study from Utqiaġvik, Alaska F. St-Denis et al. 10.3390/rs16183357
- Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network I. de Gelis et al. 10.1109/JSTARS.2021.3074068
- Passive Microwave Sea Ice Edge Displacement Error over the Eastern Canadian Arctic for the period 2013-2021 A. Soleymani et al. 10.1080/07038992.2023.2205531
- The AutoICE Challenge A. Stokholm et al. 10.5194/tc-18-3471-2024
- Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data S. Kern et al. 10.5194/tc-16-349-2022
- The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX W. Copeland et al. 10.3389/fmars.2024.1400479
- Edge displacement scores A. Melsom 10.5194/tc-15-3785-2021
- Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images N. Panchi et al. 10.1109/JSEN.2021.3084556
- Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic M. Liu et al. 10.3389/fmars.2022.986228
- RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss A. Nagi et al. 10.3390/rs13132460
- Pan-Arctic sea ice concentration from SAR and passive microwave T. Wulf et al. 10.5194/tc-18-5277-2024
- Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada B. Montpetit et al. 10.1080/07038992.2023.2247091
17 citations as recorded by crossref.
- Intercomparison of Arctic Sea Ice Backscatter and Ice Type Classification Using Ku-Band and C-Band Scatterometers Z. Zhang et al. 10.1109/TGRS.2021.3099835
- AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting A. Kucik & A. Stokholm 10.1038/s41598-023-32467-x
- Mapping the thickness of sea ice in the Arctic as an example of using data from a ship-based television complex for operational hydrometeorological support of maritime activities E. Afanasyeva et al. 10.30758/0555-2648-2022-68-2-96-117
- Potential of Lightweight Drones and Object-Oriented Image Segmentation in Forest Plantation Assessment J. Dixit et al. 10.3390/rs16091554
- Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification J. Lohse et al. 10.3390/rs13040552
- Coastal Sea Ice Concentration Derived from Marine Radar Images: A Case Study from Utqiaġvik, Alaska F. St-Denis et al. 10.3390/rs16183357
- Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network I. de Gelis et al. 10.1109/JSTARS.2021.3074068
- Passive Microwave Sea Ice Edge Displacement Error over the Eastern Canadian Arctic for the period 2013-2021 A. Soleymani et al. 10.1080/07038992.2023.2205531
- The AutoICE Challenge A. Stokholm et al. 10.5194/tc-18-3471-2024
- Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data S. Kern et al. 10.5194/tc-16-349-2022
- The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX W. Copeland et al. 10.3389/fmars.2024.1400479
- Edge displacement scores A. Melsom 10.5194/tc-15-3785-2021
- Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images N. Panchi et al. 10.1109/JSEN.2021.3084556
- Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic M. Liu et al. 10.3389/fmars.2022.986228
- RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss A. Nagi et al. 10.3390/rs13132460
- Pan-Arctic sea ice concentration from SAR and passive microwave T. Wulf et al. 10.5194/tc-18-5277-2024
- Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada B. Montpetit et al. 10.1080/07038992.2023.2247091
Latest update: 23 Nov 2024
Short summary
Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration...
