Articles | Volume 11, issue 2
https://doi.org/10.5194/tc-11-755-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-11-755-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Mar 2017
Research article |
| 23 Mar 2017
Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery
Ane S. Fors
CORRESPONDING AUTHOR
Department of Physics and Technology, University of Tromsø – The Arctic University of Norway, 9037 Tromsø, Norway
Dmitry V. Divine
Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
Department of Mathematics and Statistics, University of Tromsø – The Arctic University of Norway, 9037 Tromsø, Norway
Anthony P. Doulgeris
Department of Physics and Technology, University of Tromsø – The Arctic University of Norway, 9037 Tromsø, Norway
Angelika H. H. Renner
Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
Institute of Marine Research, 9294 Tromsø, Norway
Sebastian Gerland
Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
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Cited
12 citations as recorded by crossref.
- A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data J. Yackel et al. 10.1016/j.rse.2017.09.030
- IMPROVED OF AN AUTOMATIC IMAGE ANALYSIS METHOD FOR RECOGNIZING SEA ICE CONDITIONS DURING THE ARCTIC SUMMER MELT SEASON Y. TANAKA 10.2208/jscejoe.76.1_22
- Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter Z. Peng et al. 10.3390/rs14184538
- Discrimination of open water from sea ice in the Labrador Sea using quad-polarized synthetic aperture radar T. Xie et al. 10.1016/j.rse.2020.111948
- Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters S. Ramjan et al. 10.3390/rs10101603
- Arctic sea ice melt pond fraction in 2000–2021 derived by dynamic pixel spectral unmixing of MODIS images C. Xiong & Y. Ren 10.1016/j.isprsjprs.2023.01.023
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
- Variations in Antarctic Peninsula snow liquid water during 1999–2017 revealed by merging radiometer, scatterometer and model estimations L. Zheng et al. 10.1016/j.rse.2019.111219
- Arctic summer sea ice phenology including ponding from 1982 to 2017 X. Chen et al. 10.1007/s13131-022-1993-5
- Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter T. Geldsetzer et al. 10.1016/j.rse.2023.113725
- Polarimetric SAR Applications of Sea Ice: A Review M. Shokr & M. Dabboor 10.1109/JSTARS.2023.3295735
- Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2 S. Howell et al. 10.5194/tc-14-4675-2020
12 citations as recorded by crossref.
- A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data J. Yackel et al. 10.1016/j.rse.2017.09.030
- IMPROVED OF AN AUTOMATIC IMAGE ANALYSIS METHOD FOR RECOGNIZING SEA ICE CONDITIONS DURING THE ARCTIC SUMMER MELT SEASON Y. TANAKA 10.2208/jscejoe.76.1_22
- Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter Z. Peng et al. 10.3390/rs14184538
- Discrimination of open water from sea ice in the Labrador Sea using quad-polarized synthetic aperture radar T. Xie et al. 10.1016/j.rse.2020.111948
- Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters S. Ramjan et al. 10.3390/rs10101603
- Arctic sea ice melt pond fraction in 2000–2021 derived by dynamic pixel spectral unmixing of MODIS images C. Xiong & Y. Ren 10.1016/j.isprsjprs.2023.01.023
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
- Variations in Antarctic Peninsula snow liquid water during 1999–2017 revealed by merging radiometer, scatterometer and model estimations L. Zheng et al. 10.1016/j.rse.2019.111219
- Arctic summer sea ice phenology including ponding from 1982 to 2017 X. Chen et al. 10.1007/s13131-022-1993-5
- Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter T. Geldsetzer et al. 10.1016/j.rse.2023.113725
- Polarimetric SAR Applications of Sea Ice: A Review M. Shokr & M. Dabboor 10.1109/JSTARS.2023.3295735
- Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2 S. Howell et al. 10.5194/tc-14-4675-2020
Latest update: 17 Apr 2024
Short summary
This paper investigates the signature of melt ponds in satellite-borne synthetic aperture radar (SAR) imagery. A comparison between helicopter-borne images of drifting first-year ice and polarimetric X-band SAR images shows relations between observed melt pond fraction and several polarimetric SAR features. Melt ponds strongly influence the Arctic sea ice energy budget, and the results imply prospective opportunities for expanded monitoring of melt ponds from space.
This paper investigates the signature of melt ponds in satellite-borne synthetic aperture radar...
