Articles | Volume 12, issue 6
https://doi.org/10.5194/tc-12-1921-2018
© Author(s) 2018. 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-12-1921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jun 2018
Research article |
| 06 Jun 2018
| 06 Jun 2018
Reflective properties of melt ponds on sea ice
Aleksey Malinka
CORRESPONDING AUTHOR
Institute of Physics, National Academy of Sciences of Belarus, 220072,
pr. Nezavisimosti 68-2, Minsk, Belarus
Eleonora Zege
Institute of Physics, National Academy of Sciences of Belarus, 220072,
pr. Nezavisimosti 68-2, Minsk, Belarus
Larysa Istomina
Institute of Environmental Physics, University of Bremen,
Otto-Hahn-Allee 1, 28359 Bremen, Germany
Georg Heygster
Institute of Environmental Physics, University of Bremen,
Otto-Hahn-Allee 1, 28359 Bremen, Germany
Gunnar Spreen
Institute of Environmental Physics, University of Bremen,
Otto-Hahn-Allee 1, 28359 Bremen, Germany
Donald Perovich
Thayer School of Engineering, Dartmouth College, Hanover, New
Hampshire, USA
Chris Polashenski
Cold Regions Research and Engineering Laboratory, Engineer Research
and Development Center, Hanover, New Hampshire, USA
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Cited
22 citations as recorded by crossref.
- Retrieval of snow layer and melt pond properties on Arctic sea ice from airborne imaging spectrometer observations S. Rosenburg et al. 10.5194/amt-16-3915-2023
- Glacier Ice Surface Properties in South‐West Greenland Ice Sheet: First Estimates From PRISMA Imaging Spectroscopy Data N. Bohn et al. 10.1029/2021JG006718
- Impact of a Surface Ice Lid on the Optical Properties of Melt Ponds P. Lu et al. 10.1029/2018JC014161
- Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice H. Zhang et al. 10.3390/rs14122831
- Estimating Surface Albedo of Arctic Sea Ice Using an Ensemble Back-Propagation Neural Network: Toward a Better Consideration of Reflectance Anisotropy and Melt Ponds Y. Ding et al. 10.1109/TGRS.2022.3202046
- Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide H. Niehaus et al. 10.1029/2022GL102102
- The distribution and evolution of supraglacial lakes on 79° N Glacier (north-eastern Greenland) and interannual climatic controls J. Turton et al. 10.5194/tc-15-3877-2021
- Universality in azimuthal asymmetry of anisotropic sea-ice reflectance V. Marinyuk & S. Sheberstov 10.1364/JOSAA.467427
- The effect of low-level thin arctic clouds on shortwave irradiance: evaluation of estimates from spaceborne passive imagery with aircraft observations H. Chen et al. 10.5194/amt-14-2673-2021
- The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation J. Peng et al. 10.3390/rs10111826
- The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice M. Lamare et al. 10.5194/tc-17-737-2023
- 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
- SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models L. Mei et al. 10.5194/gmd-16-1511-2023
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- An effect of a snow cover on solar heating and melting of lake or sea ice L. Dombrovsky 10.3389/fther.2023.1354265
- 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
- Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model H. Niehaus et al. 10.5194/tc-18-933-2024
- Experimental investigation of the partitioning of radiation in the melt pond–ice–ocean system H. Zhang et al. 10.1016/j.coldregions.2023.104107
- Impact of Microstructure on Solar Radiation Transfer Within Sea Ice During Summer in the Arctic: A Model Sensitivity Study M. Yu et al. 10.3389/fmars.2022.861994
- A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data M. König & N. Oppelt 10.5194/tc-14-2567-2020
- The color of melt ponds on Arctic sea ice P. Lu et al. 10.5194/tc-12-1331-2018
- ОТРАЖАТЕЛЬНЫЕ СВОЙСТВА АРКТИЧЕСКОГО ЛЕТНЕГО ЛЬДА В ВИДИМОМ И ИНФРАКРАСНОМ ДИАПАЗОНАХ, "Фундаментальная и прикладная гидрофизика" Э. Зеге et al. 10.7868/S2073667318030024
20 citations as recorded by crossref.
- Retrieval of snow layer and melt pond properties on Arctic sea ice from airborne imaging spectrometer observations S. Rosenburg et al. 10.5194/amt-16-3915-2023
- Glacier Ice Surface Properties in South‐West Greenland Ice Sheet: First Estimates From PRISMA Imaging Spectroscopy Data N. Bohn et al. 10.1029/2021JG006718
- Impact of a Surface Ice Lid on the Optical Properties of Melt Ponds P. Lu et al. 10.1029/2018JC014161
- Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice H. Zhang et al. 10.3390/rs14122831
- Estimating Surface Albedo of Arctic Sea Ice Using an Ensemble Back-Propagation Neural Network: Toward a Better Consideration of Reflectance Anisotropy and Melt Ponds Y. Ding et al. 10.1109/TGRS.2022.3202046
- Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide H. Niehaus et al. 10.1029/2022GL102102
- The distribution and evolution of supraglacial lakes on 79° N Glacier (north-eastern Greenland) and interannual climatic controls J. Turton et al. 10.5194/tc-15-3877-2021
- Universality in azimuthal asymmetry of anisotropic sea-ice reflectance V. Marinyuk & S. Sheberstov 10.1364/JOSAA.467427
- The effect of low-level thin arctic clouds on shortwave irradiance: evaluation of estimates from spaceborne passive imagery with aircraft observations H. Chen et al. 10.5194/amt-14-2673-2021
- The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation J. Peng et al. 10.3390/rs10111826
- The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice M. Lamare et al. 10.5194/tc-17-737-2023
- 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
- SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models L. Mei et al. 10.5194/gmd-16-1511-2023
- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- An effect of a snow cover on solar heating and melting of lake or sea ice L. Dombrovsky 10.3389/fther.2023.1354265
- 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
- Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model H. Niehaus et al. 10.5194/tc-18-933-2024
- Experimental investigation of the partitioning of radiation in the melt pond–ice–ocean system H. Zhang et al. 10.1016/j.coldregions.2023.104107
- Impact of Microstructure on Solar Radiation Transfer Within Sea Ice During Summer in the Arctic: A Model Sensitivity Study M. Yu et al. 10.3389/fmars.2022.861994
- A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data M. König & N. Oppelt 10.5194/tc-14-2567-2020
Discussed (final revised paper)
Latest update: 29 Jun 2024
Short summary
Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative...
